Deep frequency-recurrent priors for inverse imaging reconstruction
•A multi-profile high-frequency transform-guided denoising autoencoder for attainting deep frequency recurrent prior.•We extract a set of multi-profile high-frequency components via a specific transformation.•The learned high-frequency prior information is incorporated into classical iterative recon...
Gespeichert in:
| Veröffentlicht in: | Signal processing Jg. 190; S. 108320 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.01.2022
|
| Schlagworte: | |
| ISSN: | 0165-1684, 1872-7557 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | •A multi-profile high-frequency transform-guided denoising autoencoder for attainting deep frequency recurrent prior.•We extract a set of multi-profile high-frequency components via a specific transformation.•The learned high-frequency prior information is incorporated into classical iterative reconstruction.
Ill-posed inverse problems in imaging remain an active research topic in several decades, with new approaches constantly emerging. Recognizing that the popular dictionary learning and convolutional sparse coding are both essentially modeling the high-frequency component of an image, which convey most of the semantic information such as texture details, in this work we propose a novel multi-profile high-frequency transform-guided denoising autoencoder for attaining deep frequency-recurrent prior (DFRP). To achieve this goal, we first extract a set of multi-profile high-frequency components via a specific transformation and add artificial Gaussian noise to these high-frequency components as training samples. Then, as the high-frequency prior information is learned, we incorporate it into classical iterative reconstruction by proximal gradient descent. Preliminary results on highly under-sampled magnetic resonance imaging and sparse-view computed tomography reconstruction demonstrated that the proposed method can efficiently reconstruct feature details and presented advantages over state-of-the-arts.
The network architecture at the training procedure and one-iteration diagram at the iterative reconstruction of DFRP. (a) The DAE network used in DFRP. (b) The illustration of DFRP at iterative reconstruction phase. The Conv layer is denoted as “C”. Additionally, the Concatenation layer is denoted as “Cat”. Here the example of MRI reconstruction is visualized. [Display omitted] |
|---|---|
| AbstractList | •A multi-profile high-frequency transform-guided denoising autoencoder for attainting deep frequency recurrent prior.•We extract a set of multi-profile high-frequency components via a specific transformation.•The learned high-frequency prior information is incorporated into classical iterative reconstruction.
Ill-posed inverse problems in imaging remain an active research topic in several decades, with new approaches constantly emerging. Recognizing that the popular dictionary learning and convolutional sparse coding are both essentially modeling the high-frequency component of an image, which convey most of the semantic information such as texture details, in this work we propose a novel multi-profile high-frequency transform-guided denoising autoencoder for attaining deep frequency-recurrent prior (DFRP). To achieve this goal, we first extract a set of multi-profile high-frequency components via a specific transformation and add artificial Gaussian noise to these high-frequency components as training samples. Then, as the high-frequency prior information is learned, we incorporate it into classical iterative reconstruction by proximal gradient descent. Preliminary results on highly under-sampled magnetic resonance imaging and sparse-view computed tomography reconstruction demonstrated that the proposed method can efficiently reconstruct feature details and presented advantages over state-of-the-arts.
The network architecture at the training procedure and one-iteration diagram at the iterative reconstruction of DFRP. (a) The DAE network used in DFRP. (b) The illustration of DFRP at iterative reconstruction phase. The Conv layer is denoted as “C”. Additionally, the Concatenation layer is denoted as “Cat”. Here the example of MRI reconstruction is visualized. [Display omitted] |
| ArticleNumber | 108320 |
| Author | He, Zhuonan Hong, Kai Liu, Qiegen Liang, Dong Wang, Yuhao Zhou, Jinjie |
| Author_xml | – sequence: 1 givenname: Zhuonan surname: He fullname: He, Zhuonan organization: Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China – sequence: 2 givenname: Kai surname: Hong fullname: Hong, Kai organization: Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China – sequence: 3 givenname: Jinjie surname: Zhou fullname: Zhou, Jinjie organization: Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China – sequence: 4 givenname: Dong surname: Liang fullname: Liang, Dong organization: Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, Chinese Academy of Sciences, Shenzhen 518055, China – sequence: 5 givenname: Yuhao surname: Wang fullname: Wang, Yuhao email: wangyuhao@ncu.edu.cn organization: Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China – sequence: 6 givenname: Qiegen surname: Liu fullname: Liu, Qiegen email: liuqiegen@ncu.edu.cn organization: Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China |
| BookMark | eNqFkM1OwzAQhC1UJNrCG3DIC6SskzhxOSBB-ZUqcYGzlTrrylVrl7VbqW-Po3DiAKddrWZGO9-EjZx3yNg1hxkHXt9sZsGu9-RnBRQ8nWRZwBkbc9kUeSNEM2LjJBM5r2V1wSYhbACAlzWM2cMj4j4zhF8HdPqUE-oDEbqY7cl6CpnxlFl3RAqY2V27tm6dJZF3IdJBR-vdJTs37Tbg1c-css_np4_Fa758f3lb3C9zXUId87msUIpWCqnLlZAdlqbqOlMXhkOl5_0iikqA5oWBxuBKNChWIEVt-NxoXU7Z7ZCryYdAaJS2se0_iNTareKgehpqowYaqqehBhrJXP0yp367lk7_2e4GG6ZiR4ukgrYJFHY2QYiq8_bvgG8m9H9J |
| CitedBy_id | crossref_primary_10_1088_1361_6501_ad11cd crossref_primary_10_1002_mp_16550 crossref_primary_10_1016_j_sigpro_2024_109746 crossref_primary_10_1109_TIM_2025_3529044 crossref_primary_10_1016_j_jmr_2022_107354 crossref_primary_10_1002_mp_16702 |
| Cites_doi | 10.1109/TIP.2017.2713099 10.1109/42.993128 10.1364/OE.24.014564 10.1002/mrm.21757 10.1109/TIP.2020.2995048 10.1109/TMI.2018.2823768 10.1109/TMI.2019.2906853 10.1109/TCI.2015.2479555 10.1109/TMI.2018.2863670 10.1109/TIP.2015.2495260 10.1056/NEJMra072149 10.1088/0031-9155/58/16/5803 10.1109/TMI.2011.2140121 10.1137/040605412 10.1118/1.595715 10.1109/TSP.2006.881199 10.1109/TNS.2004.834824 10.1109/TMI.2018.2887072 10.1109/TCI.2016.2609414 10.1109/TIP.2012.2235847 10.1002/mp.12344 10.1002/ima.22171 10.1109/TMI.2010.2090538 10.1002/mrm.27921 10.1109/TIP.2011.2108306 10.1016/j.media.2013.09.007 10.1109/TMI.2006.882141 10.1109/TIP.2019.2931240 10.1088/0031-9155/56/18/011 10.1109/TIP.2017.2662206 10.1038/nature25988 10.1109/TMI.2012.2195669 10.1109/TIP.2013.2277798 10.1109/TMI.2017.2715284 10.1002/mrm.22736 10.1088/0031-9155/53/17/021 10.1002/mrm.21391 10.1002/mrm.26977 10.1109/TIP.2014.2329449 10.1109/TBME.2015.2503756 |
| ContentType | Journal Article |
| Copyright | 2021 Elsevier B.V. |
| Copyright_xml | – notice: 2021 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.sigpro.2021.108320 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1872-7557 |
| ExternalDocumentID | 10_1016_j_sigpro_2021_108320 S0165168421003571 |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 123 1B1 1~. 1~5 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABFNM ABFRF ABMAC ABXDB ABYKQ ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F0J F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q G8K GBLVA GBOLZ HLZ HVGLF HZ~ IHE J1W JJJVA KOM LG9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SES SEW SPC SPCBC SST SSV SSZ T5K TAE TN5 WUQ XPP ZMT ~02 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABDPE ABJNI ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c306t-984e85a858c3b58de3f4ddf62f104c9f62f52450c12f07feb57e5b0856f19fcc3 |
| ISICitedReferencesCount | 6 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000704396500012&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0165-1684 |
| IngestDate | Sat Nov 29 07:23:41 EST 2025 Tue Nov 18 21:39:28 EST 2025 Fri Feb 23 02:47:09 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Sparse-view CT Denoising autoencoder network Imaging reconstruction Proximal gradient descent High-frequency component MRI reconstruction |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c306t-984e85a858c3b58de3f4ddf62f104c9f62f52450c12f07feb57e5b0856f19fcc3 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_sigpro_2021_108320 crossref_primary_10_1016_j_sigpro_2021_108320 elsevier_sciencedirect_doi_10_1016_j_sigpro_2021_108320 |
| PublicationCentury | 2000 |
| PublicationDate | January 2022 2022-01-00 |
| PublicationDateYYYYMMDD | 2022-01-01 |
| PublicationDate_xml | – month: 01 year: 2022 text: January 2022 |
| PublicationDecade | 2020 |
| PublicationTitle | Signal processing |
| PublicationYear | 2022 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Zeiler, Krishnan, Taylor, Fergus (bib0016) 2010; 10 Hammernik, Klatzer, Kobler, Recht, Sodickson, Pock, Knoll (bib0024) 2018; 79 Liu, Wang, Ying, Peng, Zhu, Liang (bib0021) 2013; 22 Wohlberg (bib0047) 2016; 25 Zhang, Dai, Li, Koniusz (bib0052) 2019 Tihonov (bib0013) 1963; 4 Guerquin-Kern, Haberlin, Pruessmann, Unser (bib0004) 2011; 30 Alain, Bengio (bib0043) 2014; 15 Li, Li, Wang, Wen, Lu, Hsieh, Liang (bib0055) 2004; 51 Wu, Du, Mei (bib0048) 2016; 26 Parikh, Boyd (bib0054) 2014; 1 Brenner, Hall (bib0063) 2007; 357 Aharon, Elad, Bruckstein (bib0020) 2006; 54 (bib0027) 2018; 5 Dong, Zhang, Shi, Wu (bib0044) 2011; 20 Osher (bib0014) 1992; 60 Nah, Kim, Lee (bib0049) 2017 Han, Ye (bib0025) 2018; 37 Mao, Shen, Yang (bib0053) 2016; 29 Tao, Gao, Shen, Wang, Jia (bib0050) 2018 Elbakri, Fessler (bib0058) 2002; 21 Lu, Hsiao, Li, Liang (bib0056) 2001; 3 Zoran, Weiss (bib0012) 2011 Liu, Yang, Cheng, Wang, Zhang, Liang (bib0030) 2020; 83 Li, Qin, Xiao, Liu, Wang, Liang (bib0029) 2020; 29 Huang, Shao, Frangi (bib0038) 2017 Zhan, Cai, Guo, Liu, Chen, Qu (bib0046) 2016; 63 Tian, Jia, Yuan, Pan, Jiang (bib0033) 2011; 56 Vincent, Larochelle, Lajoie, Bengio, Manzagol (bib0042) 2010; 11 Lustig, Donoho, Pauly (bib0001) 2007; 58 Qin, Schlemper, Caballero, Price, Hajnal, Rueckert (bib0040) 2018; 38 Bao, Xia, Yang, Chen, Chen, Xi, Wang (bib0041) 2019; 38 Chen, Zhang, K.a, Lin, Chen, Liao, Wang (bib0039) 2017; 36 Jung, Sung, Nayak, Kim, Ye (bib0003) 2009; 61 Quan, Jeong (bib0037) 2016 Chen, Yin, Shi, Shu, Luo, Coatrieux, Toumoulin (bib0036) 2013; 58 Schlemper, Caballero, Hajnal, Price, Rueckert (bib0062) 2017 Ravishankar, Bresler (bib0034) 2011; 30 McGaffin, Fessler (bib0005) 2015; l Sun (bib0006) 2016; 29 Buades, Coll, Morel (bib0018) 2005; 2 Timofte, Agustsson (bib0059) 2017 Siddon (bib0064) 1985; 12 Dong, Zhang, Shi, Li (bib0019) 2013; 22 Ying, Xu, Liang (bib0031) 2004 Zhang, Zuo, Chen, Meng, Zhang (bib0026) 2017; 26 Tezcan, Baumgartner, Konukoglu (bib0028) 2019; 38 Zhang, Patel (bib0017) 2016 Dong, Shi, Li, Ma, Huang (bib0061) 2014; 23 Wohlberg (bib0022) 2016 Zhu, Liu, Cauley, Rosen, Rosen (bib0010) 2018; 555 Xu, Yu, Mou, Zhang, Hsieh, Wang (bib0035) 2012; 31 (bib0051) 2020; 29 Kang, Min, Ye (bib0009) 2017; 44 Carrera, Boracchi, Foi, Wohlberg (bib0045) 2015 Jin, McCann, Froustey, Unser (bib0023) 2017; 26 Zhang, Xi, Yang, Cong, Zhou, Wang (bib0007) 2016; 2 Sidky, Pan (bib0002) 2008; 53 Wang, Li, Lu, Liang (bib0057) 2006; 25 Osher, Burger, Goldfarb, Xu, Yin (bib0015) 2005; 4 Qu, Hou, Lam, Guo, Zhong, Chen (bib0060) 2014; 18 Roth, Black (bib0011) 2005 Liang, Wang, Chang, Ying (bib0032) 2011; 65 Tan, Le (bib0065) 2019 McCann, Nilchian, Stampanoni, Unser (bib0008) 2016; 24 Qu (10.1016/j.sigpro.2021.108320_bib0060) 2014; 18 Tezcan (10.1016/j.sigpro.2021.108320_bib0028) 2019; 38 Wohlberg (10.1016/j.sigpro.2021.108320_bib0047) 2016; 25 Siddon (10.1016/j.sigpro.2021.108320_bib0064) 1985; 12 Zhan (10.1016/j.sigpro.2021.108320_bib0046) 2016; 63 Qin (10.1016/j.sigpro.2021.108320_bib0040) 2018; 38 Zhang (10.1016/j.sigpro.2021.108320_bib0007) 2016; 2 Osher (10.1016/j.sigpro.2021.108320_bib0014) 1992; 60 (10.1016/j.sigpro.2021.108320_bib0027) 2018; 5 Quan (10.1016/j.sigpro.2021.108320_bib0037) 2016 Timofte (10.1016/j.sigpro.2021.108320_bib0059) 2017 Zeiler (10.1016/j.sigpro.2021.108320_bib0016) 2010; 10 Liu (10.1016/j.sigpro.2021.108320_bib0030) 2020; 83 Brenner (10.1016/j.sigpro.2021.108320_bib0063) 2007; 357 Schlemper (10.1016/j.sigpro.2021.108320_bib0062) 2017 Li (10.1016/j.sigpro.2021.108320_bib0029) 2020; 29 Guerquin-Kern (10.1016/j.sigpro.2021.108320_bib0004) 2011; 30 Hammernik (10.1016/j.sigpro.2021.108320_bib0024) 2018; 79 Sun (10.1016/j.sigpro.2021.108320_bib0006) 2016; 29 Tihonov (10.1016/j.sigpro.2021.108320_bib0013) 1963; 4 (10.1016/j.sigpro.2021.108320_bib0051) 2020; 29 Zhu (10.1016/j.sigpro.2021.108320_bib0010) 2018; 555 Mao (10.1016/j.sigpro.2021.108320_bib0053) 2016; 29 Wang (10.1016/j.sigpro.2021.108320_bib0057) 2006; 25 Kang (10.1016/j.sigpro.2021.108320_bib0009) 2017; 44 Dong (10.1016/j.sigpro.2021.108320_bib0044) 2011; 20 Zhang (10.1016/j.sigpro.2021.108320_bib0052) 2019 McGaffin (10.1016/j.sigpro.2021.108320_bib0005) 2015; l Elbakri (10.1016/j.sigpro.2021.108320_bib0058) 2002; 21 Aharon (10.1016/j.sigpro.2021.108320_bib0020) 2006; 54 Tian (10.1016/j.sigpro.2021.108320_bib0033) 2011; 56 Alain (10.1016/j.sigpro.2021.108320_bib0043) 2014; 15 Osher (10.1016/j.sigpro.2021.108320_bib0015) 2005; 4 Vincent (10.1016/j.sigpro.2021.108320_bib0042) 2010; 11 Buades (10.1016/j.sigpro.2021.108320_bib0018) 2005; 2 Ravishankar (10.1016/j.sigpro.2021.108320_bib0034) 2011; 30 Sidky (10.1016/j.sigpro.2021.108320_bib0002) 2008; 53 Tao (10.1016/j.sigpro.2021.108320_bib0050) 2018 Liu (10.1016/j.sigpro.2021.108320_bib0021) 2013; 22 Jung (10.1016/j.sigpro.2021.108320_bib0003) 2009; 61 Xu (10.1016/j.sigpro.2021.108320_bib0035) 2012; 31 Dong (10.1016/j.sigpro.2021.108320_bib0061) 2014; 23 Li (10.1016/j.sigpro.2021.108320_bib0055) 2004; 51 Ying (10.1016/j.sigpro.2021.108320_bib0031) 2004 Lu (10.1016/j.sigpro.2021.108320_bib0056) 2001; 3 Chen (10.1016/j.sigpro.2021.108320_bib0039) 2017; 36 Tan (10.1016/j.sigpro.2021.108320_bib0065) 2019 Liang (10.1016/j.sigpro.2021.108320_bib0032) 2011; 65 Parikh (10.1016/j.sigpro.2021.108320_bib0054) 2014; 1 Chen (10.1016/j.sigpro.2021.108320_bib0036) 2013; 58 Han (10.1016/j.sigpro.2021.108320_bib0025) 2018; 37 Jin (10.1016/j.sigpro.2021.108320_bib0023) 2017; 26 Carrera (10.1016/j.sigpro.2021.108320_bib0045) 2015 Lustig (10.1016/j.sigpro.2021.108320_bib0001) 2007; 58 Zhang (10.1016/j.sigpro.2021.108320_bib0026) 2017; 26 Wohlberg (10.1016/j.sigpro.2021.108320_bib0022) 2016 Bao (10.1016/j.sigpro.2021.108320_bib0041) 2019; 38 Dong (10.1016/j.sigpro.2021.108320_bib0019) 2013; 22 Huang (10.1016/j.sigpro.2021.108320_bib0038) 2017 Zhang (10.1016/j.sigpro.2021.108320_bib0017) 2016 Wu (10.1016/j.sigpro.2021.108320_bib0048) 2016; 26 Zoran (10.1016/j.sigpro.2021.108320_bib0012) 2011 Roth (10.1016/j.sigpro.2021.108320_bib0011) 2005 Nah (10.1016/j.sigpro.2021.108320_bib0049) 2017 McCann (10.1016/j.sigpro.2021.108320_bib0008) 2016; 24 |
| References_xml | – year: 2019 ident: bib0065 article-title: Efficientnet: Rethinking model scaling for convolutional neural networks publication-title: Proceedings of the International Conference on Machine Learning – start-page: 1056 year: 2004 end-page: 1059 ident: bib0031 article-title: On Tikhonov regularization for image reconstruction in parallel MRI publication-title: Proceedings of the International Conference of IEEE Engineering in Medicine and Biology Society – volume: 25 start-page: 301 year: 2016 end-page: 315 ident: bib0047 article-title: Efficient algorithms for convolutional sparse representations publication-title: IEEE Trans. Image Process. – volume: 10 start-page: 2528 year: 2010 end-page: 2535 ident: bib0016 article-title: Deconvolutional networks publication-title: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 58 start-page: 5803 year: 2013 end-page: 5820 ident: bib0036 article-title: Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing publication-title: Phys. Med. Biol. – volume: 30 start-page: 1649 year: 2011 end-page: 1660 ident: bib0004 article-title: A fast wavelet-based reconstruction method for magnetic resonance imaging publication-title: IEEE Trans. Med. Imaging – volume: 4 start-page: 1035 year: 1963 end-page: 1038 ident: bib0013 article-title: Solution of incorrectly formulated problems and the regularization method publication-title: Sov. Math. Dokl. – start-page: 479 year: 2011 end-page: 486 ident: bib0012 article-title: From learning models of natural image patches to whole image restoration publication-title: Proceedings of the International Conference on Computer Vision – volume: 26 start-page: 173 year: 2016 end-page: 178 ident: bib0048 article-title: Filter-based compressed sensing MRI reconstruction publication-title: Int. J. Imaging Syst. Technol. – start-page: 1 year: 2016 end-page: 5 ident: bib0022 article-title: Convolutional sparse representations as an image model for impulse noise restoration publication-title: Proceedings of the Multidimensional Signal Processing Workshop – start-page: 1110 year: 2017 end-page: 1121 ident: bib0059 article-title: Ntire 2017 challenge on single image super-resolution: Methods and results publication-title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) – start-page: 1 year: 2019 end-page: 10 ident: bib0052 article-title: Deep stacked hierarchical multi-patch network for image deblurring publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 26 start-page: 3142 year: 2017 end-page: 3155 ident: bib0026 article-title: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising publication-title: IEEE Trans. Image Process. – volume: 51 start-page: 2505 year: 2004 end-page: 2513 ident: bib0055 article-title: Nonlinear sinogram smoothing for low-dose X-ray CT publication-title: IEEE Trans. Nucl. Sci. – start-page: 860 year: 2005 end-page: 867 ident: bib0011 article-title: Fields of experts: a framework for learning image priors publication-title: Proceedings of the Computer Vision and Pattern Recognition – start-page: 518 year: 2016 end-page: 521 ident: bib0037 article-title: Compressed sensing reconstruction of dynamic contrast enhanced MRI using GPU-accelerated convolutional sparse coding publication-title: Proceedings of the IEEE 13th International Symposium on Biomedical Imaging – volume: 25 start-page: 1272 year: 2006 end-page: 1283 ident: bib0057 article-title: Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomography publication-title: IEEE Trans. Med. Imaging – volume: 37 start-page: 1418 year: 2018 end-page: 1429 ident: bib0025 article-title: Framing u-net via deep convolutional framelets: application to sparse-view CT publication-title: IEEE Trans. Med. Imaging – volume: 2 start-page: 60 year: 2005 end-page: 65 ident: bib0018 article-title: A non-local algorithm for image denoising publication-title: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR) – start-page: 647 year: 2017 end-page: 658 ident: bib0062 article-title: A deep cascade of convolutional neural networks for MR image reconstruction publication-title: Proceedings of the International Cconference Information Processing – start-page: 8174 year: 2018 end-page: 8182 ident: bib0050 article-title: Scale-recurrent network for deep image deblurring publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 555 start-page: 487 year: 2018 ident: bib0010 article-title: Image reconstruction by domain-transform manifold learning publication-title: Nature – volume: 12 start-page: 252 year: 1985 end-page: 255 ident: bib0064 article-title: Fast calculation of the exact radiological path for a three-dimensional CT array publication-title: Med. Phys. – volume: 56 start-page: 5949 year: 2011 ident: bib0033 article-title: Low-dose CT reconstruction via edge-preserving total variation regularization publication-title: Phys. Med. Biol. – volume: 3 start-page: 1662 year: 2001 end-page: 1666 ident: bib0056 article-title: Noise properties of low-dose CT projections and noise treatment by scale transformations publication-title: IEEE Nucl. Sci. Symp. – volume: 29 start-page: 2802 year: 2016 end-page: 2810 ident: bib0053 article-title: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections publication-title: In Neural Inf. Process. Syst. – volume: 31 start-page: 1682 year: 2012 end-page: 1697 ident: bib0035 article-title: Low-dose X-ray CT reconstruction via dictionary learning publication-title: IEEE Trans. Med. Imaging – start-page: 257 year: 2017 end-page: 265 ident: bib0049 article-title: Deep multi-scale convolutional neural network for dynamic scene deblurring publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 24 start-page: 14564 year: 2016 end-page: 14581 ident: bib0008 article-title: Fast 3D reconstruction method for differential phase contrast X-ray CT publication-title: Opt. Express – volume: 22 start-page: 1620 year: 2013 end-page: 1630 ident: bib0019 article-title: Nonlocally centralized sparse representation for image restoration publication-title: IEEE Trans. Image Process. – volume: 22 start-page: 4652 year: 2013 end-page: 4663 ident: bib0021 article-title: Adaptive dictionary learning in sparse gradient domain for image recovery publication-title: IEEE Trans. Image Process. – volume: 38 start-page: 280 year: 2018 end-page: 290 ident: bib0040 article-title: Convolutional recurrent neural networks for dynamic MR image reconstruction publication-title: IEEE Trans. Med. Imaging – start-page: 6070 year: 2017 end-page: 6079 ident: bib0038 article-title: Simultaneous super-resolution and cross-modality synthesis of 3D medical images using weakly-supervised joint convolutional sparse coding publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 54 start-page: 4311 year: 2006 end-page: 4322 ident: bib0020 article-title: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation publication-title: IEEE Trans. Signal Process. – volume: 36 start-page: 2524 year: 2017 end-page: 2535 ident: bib0039 article-title: Low-dose CT with a residual encoder-decoder convolutional neural network publication-title: IEEE Trans. Med. Imaging – volume: 44 start-page: 360 year: 2017 end-page: 375 ident: bib0009 article-title: A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction publication-title: Med. Phys. – volume: 1 start-page: 127 year: 2014 end-page: 239 ident: bib0054 publication-title: Proximal Algorithms – volume: 38 start-page: 1633 year: 2019 end-page: 1642 ident: bib0028 article-title: MR image reconstruction using deep density priors publication-title: IEEE Trans. Med. Imaging – volume: 2 start-page: 510 year: 2016 end-page: 523 ident: bib0007 article-title: Spectral CT reconstruction with image sparsity and spectral mean publication-title: IEEE Trans. Comput. Imaging – volume: 58 start-page: 1182 year: 2007 end-page: 1195 ident: bib0001 article-title: Sparse MRI: the application of compressed sensing for rapid MR imaging publication-title: Magn. Reson. Med. – volume: 29 start-page: 10 year: 2016 end-page: 18 ident: bib0006 publication-title: Deep ADMM-net for compressive sensing MRI. Neural information processing systems – volume: 26 start-page: 4509 year: 2017 end-page: 4522 ident: bib0023 article-title: Deep convolutional neural network for inverse problems in imaging publication-title: IEEE Trans. Image Process. – volume: 53 start-page: 4777 year: 2008 end-page: 4807 ident: bib0002 article-title: Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization publication-title: Phys. Med. Biol. – volume: 63 start-page: 1850 year: 2016 end-page: 1861 ident: bib0046 article-title: Fast multiclass dictionaries learning with geometrical directions in MRI reconstruction publication-title: IEEE Trans. Biomed. Eng. – volume: 29 start-page: 142 year: 2020 end-page: 156 ident: bib0029 article-title: Multi-channel and Multi-model based autoencoding prior for grayscale image restoration publication-title: IEEE Trans. Image Process. – volume: 38 start-page: 2607 year: 2019 end-page: 2619 ident: bib0041 article-title: Convolutional sparse coding for compressed sensing CT reconstruction publication-title: IEEE Trans. Med. Imaging – volume: 83 start-page: 322 year: 2020 end-page: 336 ident: bib0030 article-title: Highly undersampled magnetic resonance imaging reconstruction using autoencoding priors publication-title: Magn. Reson. Med. – volume: 60 start-page: 259 year: 1992 end-page: 268 ident: bib0014 article-title: Nonlinear total variation based noise removal algorithms publication-title: Phys. D Nonlinear Phenom. – volume: 5 start-page: 1 year: 2018 end-page: 10 ident: bib0027 article-title: Image restoration using autoencoding priors publication-title: VISIGRAPP – volume: 65 start-page: 1384 year: 2011 end-page: 1392 ident: bib0032 article-title: Sensitivity encoding reconstruction with nonlocal total variation regularization publication-title: Magn. Reson. Med. – volume: 21 start-page: 89 year: 2002 end-page: 99 ident: bib0058 article-title: Statistical image reconstruction for polyenergetic X-ray computed tomography publication-title: IEEE Trans. Med. Imaging – volume: 4 start-page: 460 year: 2005 end-page: 489 ident: bib0015 article-title: An iterative regularization method for total variation-based image restoration publication-title: Multiscale Model. Simul. – year: 2016 ident: bib0017 article-title: Convolutional sparse coding-based image decomposition publication-title: Proceedings of the British Machine Vision Conference – volume: 30 start-page: 1028 year: 2011 end-page: 1041 ident: bib0034 article-title: MR image reconstruction from highly undersampled k-space data by dictionary learning publication-title: IEEE Trans. Med. Imaging – volume: 18 start-page: 843 year: 2014 end-page: 856 ident: bib0060 article-title: Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator publication-title: Med. Image Anal. – volume: 15 start-page: 3743 year: 2014 end-page: 3773 ident: bib0043 article-title: What regularized auto-encoders learn from the data-generating distribution publication-title: J. Mach. Learn. Res. – volume: 61 start-page: 103 year: 2009 end-page: 116 ident: bib0003 article-title: k-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI publication-title: Magn. Reson. Med. – volume: 11 start-page: 3371 year: 2010 end-page: 3408 ident: bib0042 article-title: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – volume: 79 start-page: 3055 year: 2018 end-page: 3071 ident: bib0024 article-title: Learning a variational network for reconstruction of accelerated MRI data publication-title: Magn. Reson. Med. – volume: 357 start-page: 2277 year: 2007 end-page: 2284 ident: bib0063 article-title: Computed tomography-an increasing source of radiation exposure publication-title: N. Engl. J. Med. – volume: 23 start-page: 3618 year: 2014 end-page: 3632 ident: bib0061 article-title: Compressive sensing via nonlocal low-rank regularization publication-title: IEEE Trans. Image Process. – volume: 29 start-page: 6885 year: 2020 end-page: 6897 ident: bib0051 article-title: Dark and bright channel prior embedded network for dynamic scene deblurring publication-title: IEEE Trans. Image Process. – volume: l start-page: 186 year: 2015 end-page: 199 ident: bib0005 article-title: Alternating dual updates algorithm for X-ray CT reconstruction on the GPU publication-title: IEEE Trans. Comput. Imaging – start-page: 1 year: 2015 end-page: 8 ident: bib0045 article-title: Detecting anomalous structures by convolutional sparse models publication-title: Proceedings of the International Joint Conference on Neural Network – volume: 20 start-page: 1838 year: 2011 end-page: 1857 ident: bib0044 article-title: Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization publication-title: IEEE Trans. Image Process. – volume: 26 start-page: 4509 issue: 9 year: 2017 ident: 10.1016/j.sigpro.2021.108320_bib0023 article-title: Deep convolutional neural network for inverse problems in imaging publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2713099 – volume: 21 start-page: 89 issue: 2 year: 2002 ident: 10.1016/j.sigpro.2021.108320_bib0058 article-title: Statistical image reconstruction for polyenergetic X-ray computed tomography publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.993128 – volume: 24 start-page: 14564 issue: 13 year: 2016 ident: 10.1016/j.sigpro.2021.108320_bib0008 article-title: Fast 3D reconstruction method for differential phase contrast X-ray CT publication-title: Opt. Express doi: 10.1364/OE.24.014564 – year: 2016 ident: 10.1016/j.sigpro.2021.108320_bib0017 article-title: Convolutional sparse coding-based image decomposition – volume: 61 start-page: 103 issue: 1 year: 2009 ident: 10.1016/j.sigpro.2021.108320_bib0003 article-title: k-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI publication-title: Magn. Reson. Med. doi: 10.1002/mrm.21757 – volume: 29 start-page: 6885 year: 2020 ident: 10.1016/j.sigpro.2021.108320_bib0051 article-title: Dark and bright channel prior embedded network for dynamic scene deblurring publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2020.2995048 – volume: 3 start-page: 1662 year: 2001 ident: 10.1016/j.sigpro.2021.108320_bib0056 article-title: Noise properties of low-dose CT projections and noise treatment by scale transformations publication-title: IEEE Nucl. Sci. Symp. – start-page: 1 year: 2015 ident: 10.1016/j.sigpro.2021.108320_bib0045 article-title: Detecting anomalous structures by convolutional sparse models – volume: 37 start-page: 1418 issue: 6 year: 2018 ident: 10.1016/j.sigpro.2021.108320_bib0025 article-title: Framing u-net via deep convolutional framelets: application to sparse-view CT publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2018.2823768 – volume: 38 start-page: 2607 issue: 11 year: 2019 ident: 10.1016/j.sigpro.2021.108320_bib0041 article-title: Convolutional sparse coding for compressed sensing CT reconstruction publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2019.2906853 – start-page: 518 year: 2016 ident: 10.1016/j.sigpro.2021.108320_bib0037 article-title: Compressed sensing reconstruction of dynamic contrast enhanced MRI using GPU-accelerated convolutional sparse coding – start-page: 1056 year: 2004 ident: 10.1016/j.sigpro.2021.108320_bib0031 article-title: On Tikhonov regularization for image reconstruction in parallel MRI – start-page: 860 year: 2005 ident: 10.1016/j.sigpro.2021.108320_bib0011 article-title: Fields of experts: a framework for learning image priors – volume: l start-page: 186 issue: 3 year: 2015 ident: 10.1016/j.sigpro.2021.108320_bib0005 article-title: Alternating dual updates algorithm for X-ray CT reconstruction on the GPU publication-title: IEEE Trans. Comput. Imaging doi: 10.1109/TCI.2015.2479555 – volume: 38 start-page: 280 issue: 1 year: 2018 ident: 10.1016/j.sigpro.2021.108320_bib0040 article-title: Convolutional recurrent neural networks for dynamic MR image reconstruction publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2018.2863670 – volume: 25 start-page: 301 issue: 1 year: 2016 ident: 10.1016/j.sigpro.2021.108320_bib0047 article-title: Efficient algorithms for convolutional sparse representations publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2015.2495260 – volume: 357 start-page: 2277 issue: 22 year: 2007 ident: 10.1016/j.sigpro.2021.108320_bib0063 article-title: Computed tomography-an increasing source of radiation exposure publication-title: N. Engl. J. Med. doi: 10.1056/NEJMra072149 – volume: 58 start-page: 5803 issue: 16 year: 2013 ident: 10.1016/j.sigpro.2021.108320_bib0036 article-title: Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing publication-title: Phys. Med. Biol. doi: 10.1088/0031-9155/58/16/5803 – volume: 30 start-page: 1649 issue: 9 year: 2011 ident: 10.1016/j.sigpro.2021.108320_bib0004 article-title: A fast wavelet-based reconstruction method for magnetic resonance imaging publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2011.2140121 – year: 2019 ident: 10.1016/j.sigpro.2021.108320_bib0065 article-title: Efficientnet: Rethinking model scaling for convolutional neural networks – volume: 4 start-page: 460 issue: 2 year: 2005 ident: 10.1016/j.sigpro.2021.108320_bib0015 article-title: An iterative regularization method for total variation-based image restoration publication-title: Multiscale Model. Simul. doi: 10.1137/040605412 – start-page: 6070 year: 2017 ident: 10.1016/j.sigpro.2021.108320_bib0038 article-title: Simultaneous super-resolution and cross-modality synthesis of 3D medical images using weakly-supervised joint convolutional sparse coding – volume: 12 start-page: 252 issue: 2 year: 1985 ident: 10.1016/j.sigpro.2021.108320_bib0064 article-title: Fast calculation of the exact radiological path for a three-dimensional CT array publication-title: Med. Phys. doi: 10.1118/1.595715 – volume: 54 start-page: 4311 issue: 11 year: 2006 ident: 10.1016/j.sigpro.2021.108320_bib0020 article-title: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2006.881199 – volume: 4 start-page: 1035 year: 1963 ident: 10.1016/j.sigpro.2021.108320_bib0013 article-title: Solution of incorrectly formulated problems and the regularization method publication-title: Sov. Math. Dokl. – volume: 51 start-page: 2505 issue: 5 year: 2004 ident: 10.1016/j.sigpro.2021.108320_bib0055 article-title: Nonlinear sinogram smoothing for low-dose X-ray CT publication-title: IEEE Trans. Nucl. Sci. doi: 10.1109/TNS.2004.834824 – start-page: 1 year: 2019 ident: 10.1016/j.sigpro.2021.108320_bib0052 article-title: Deep stacked hierarchical multi-patch network for image deblurring – volume: 10 start-page: 2528 year: 2010 ident: 10.1016/j.sigpro.2021.108320_bib0016 article-title: Deconvolutional networks – start-page: 257 year: 2017 ident: 10.1016/j.sigpro.2021.108320_bib0049 article-title: Deep multi-scale convolutional neural network for dynamic scene deblurring – volume: 38 start-page: 1633 issue: 99 year: 2019 ident: 10.1016/j.sigpro.2021.108320_bib0028 article-title: MR image reconstruction using deep density priors publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2018.2887072 – volume: 2 start-page: 510 issue: 4 year: 2016 ident: 10.1016/j.sigpro.2021.108320_bib0007 article-title: Spectral CT reconstruction with image sparsity and spectral mean publication-title: IEEE Trans. Comput. Imaging doi: 10.1109/TCI.2016.2609414 – volume: 22 start-page: 1620 issue: 4 year: 2013 ident: 10.1016/j.sigpro.2021.108320_bib0019 article-title: Nonlocally centralized sparse representation for image restoration publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2012.2235847 – volume: 44 start-page: 360 issue: 10 year: 2017 ident: 10.1016/j.sigpro.2021.108320_bib0009 article-title: A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction publication-title: Med. Phys. doi: 10.1002/mp.12344 – volume: 26 start-page: 173 issue: 3 year: 2016 ident: 10.1016/j.sigpro.2021.108320_bib0048 article-title: Filter-based compressed sensing MRI reconstruction publication-title: Int. J. Imaging Syst. Technol. doi: 10.1002/ima.22171 – volume: 30 start-page: 1028 issue: 5 year: 2011 ident: 10.1016/j.sigpro.2021.108320_bib0034 article-title: MR image reconstruction from highly undersampled k-space data by dictionary learning publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2010.2090538 – start-page: 1 year: 2016 ident: 10.1016/j.sigpro.2021.108320_bib0022 article-title: Convolutional sparse representations as an image model for impulse noise restoration – volume: 83 start-page: 322 issue: 1 year: 2020 ident: 10.1016/j.sigpro.2021.108320_bib0030 article-title: Highly undersampled magnetic resonance imaging reconstruction using autoencoding priors publication-title: Magn. Reson. Med. doi: 10.1002/mrm.27921 – volume: 20 start-page: 1838 issue: 7 year: 2011 ident: 10.1016/j.sigpro.2021.108320_bib0044 article-title: Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2011.2108306 – volume: 15 start-page: 3743 issue: 1 year: 2014 ident: 10.1016/j.sigpro.2021.108320_bib0043 article-title: What regularized auto-encoders learn from the data-generating distribution publication-title: J. Mach. Learn. Res. – volume: 18 start-page: 843 issue: 6 year: 2014 ident: 10.1016/j.sigpro.2021.108320_bib0060 article-title: Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator publication-title: Med. Image Anal. doi: 10.1016/j.media.2013.09.007 – volume: 25 start-page: 1272 issue: 10 year: 2006 ident: 10.1016/j.sigpro.2021.108320_bib0057 article-title: Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomography publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2006.882141 – start-page: 647 year: 2017 ident: 10.1016/j.sigpro.2021.108320_bib0062 article-title: A deep cascade of convolutional neural networks for MR image reconstruction – volume: 29 start-page: 142 year: 2020 ident: 10.1016/j.sigpro.2021.108320_bib0029 article-title: Multi-channel and Multi-model based autoencoding prior for grayscale image restoration publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2019.2931240 – volume: 56 start-page: 5949 issue: 18 year: 2011 ident: 10.1016/j.sigpro.2021.108320_bib0033 article-title: Low-dose CT reconstruction via edge-preserving total variation regularization publication-title: Phys. Med. Biol. doi: 10.1088/0031-9155/56/18/011 – volume: 26 start-page: 3142 issue: 7 year: 2017 ident: 10.1016/j.sigpro.2021.108320_bib0026 article-title: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2662206 – volume: 29 start-page: 2802 year: 2016 ident: 10.1016/j.sigpro.2021.108320_bib0053 article-title: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections publication-title: In Neural Inf. Process. Syst. – volume: 555 start-page: 487 issue: 7697 year: 2018 ident: 10.1016/j.sigpro.2021.108320_bib0010 article-title: Image reconstruction by domain-transform manifold learning publication-title: Nature doi: 10.1038/nature25988 – volume: 5 start-page: 1 year: 2018 ident: 10.1016/j.sigpro.2021.108320_bib0027 article-title: Image restoration using autoencoding priors publication-title: VISIGRAPP – volume: 31 start-page: 1682 issue: 9 year: 2012 ident: 10.1016/j.sigpro.2021.108320_bib0035 article-title: Low-dose X-ray CT reconstruction via dictionary learning publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2012.2195669 – volume: 22 start-page: 4652 issue: 12 year: 2013 ident: 10.1016/j.sigpro.2021.108320_bib0021 article-title: Adaptive dictionary learning in sparse gradient domain for image recovery publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2013.2277798 – volume: 1 start-page: 127 year: 2014 ident: 10.1016/j.sigpro.2021.108320_bib0054 – volume: 36 start-page: 2524 issue: 12 year: 2017 ident: 10.1016/j.sigpro.2021.108320_bib0039 article-title: Low-dose CT with a residual encoder-decoder convolutional neural network publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2017.2715284 – volume: 60 start-page: 259 issue: 1–4 year: 1992 ident: 10.1016/j.sigpro.2021.108320_bib0014 article-title: Nonlinear total variation based noise removal algorithms publication-title: Phys. D Nonlinear Phenom. – volume: 65 start-page: 1384 issue: 5 year: 2011 ident: 10.1016/j.sigpro.2021.108320_bib0032 article-title: Sensitivity encoding reconstruction with nonlocal total variation regularization publication-title: Magn. Reson. Med. doi: 10.1002/mrm.22736 – volume: 53 start-page: 4777 issue: 17 year: 2008 ident: 10.1016/j.sigpro.2021.108320_bib0002 article-title: Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization publication-title: Phys. Med. Biol. doi: 10.1088/0031-9155/53/17/021 – volume: 29 start-page: 10 year: 2016 ident: 10.1016/j.sigpro.2021.108320_bib0006 publication-title: Deep ADMM-net for compressive sensing MRI. Neural information processing systems – start-page: 1110 year: 2017 ident: 10.1016/j.sigpro.2021.108320_bib0059 article-title: Ntire 2017 challenge on single image super-resolution: Methods and results – volume: 58 start-page: 1182 issue: 6 year: 2007 ident: 10.1016/j.sigpro.2021.108320_bib0001 article-title: Sparse MRI: the application of compressed sensing for rapid MR imaging publication-title: Magn. Reson. Med. doi: 10.1002/mrm.21391 – volume: 11 start-page: 3371 issue: 12 year: 2010 ident: 10.1016/j.sigpro.2021.108320_bib0042 article-title: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – volume: 79 start-page: 3055 issue: 6 year: 2018 ident: 10.1016/j.sigpro.2021.108320_bib0024 article-title: Learning a variational network for reconstruction of accelerated MRI data publication-title: Magn. Reson. Med. doi: 10.1002/mrm.26977 – start-page: 479 year: 2011 ident: 10.1016/j.sigpro.2021.108320_bib0012 article-title: From learning models of natural image patches to whole image restoration – volume: 23 start-page: 3618 issue: 8 year: 2014 ident: 10.1016/j.sigpro.2021.108320_bib0061 article-title: Compressive sensing via nonlocal low-rank regularization publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2014.2329449 – volume: 2 start-page: 60 year: 2005 ident: 10.1016/j.sigpro.2021.108320_bib0018 article-title: A non-local algorithm for image denoising – volume: 63 start-page: 1850 issue: 9 year: 2016 ident: 10.1016/j.sigpro.2021.108320_bib0046 article-title: Fast multiclass dictionaries learning with geometrical directions in MRI reconstruction publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2015.2503756 – start-page: 8174 year: 2018 ident: 10.1016/j.sigpro.2021.108320_bib0050 article-title: Scale-recurrent network for deep image deblurring |
| SSID | ssj0001360 |
| Score | 2.390224 |
| Snippet | •A multi-profile high-frequency transform-guided denoising autoencoder for attainting deep frequency recurrent prior.•We extract a set of multi-profile... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 108320 |
| SubjectTerms | Denoising autoencoder network High-frequency component Imaging reconstruction MRI reconstruction Proximal gradient descent Sparse-view CT |
| Title | Deep frequency-recurrent priors for inverse imaging reconstruction |
| URI | https://dx.doi.org/10.1016/j.sigpro.2021.108320 |
| Volume | 190 |
| WOSCitedRecordID | wos000704396500012&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-7557 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001360 issn: 0165-1684 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELZQywEOiKcoBZQDt8pVYseJfSy0qEBVIbWgvUWJH8Wrko12W9T-e8aPeBe24iVxiaIojpP5RvY3k3kg9EpVTBGlCwx7Y45LJRTm1LSYdKJrRclL4l0Dn4_q42M-mYiPsdvowrcTqPueX12J4b9CDdcAbJc6-xdwp4fCBTgH0OEIsMPxj4Df13rYMfMQIn2N586h7kswDXPrOusYH4HuojH0jv0amhR5sziVkl0lrCf2zPHVIeQTjPucd576_xpfLoHKJwU7jAG-H1q79EjPLr2q2H5qkxYd2ein3p_FR0bPAyE_eR7WU2KCh7JiuKhC37ddHVZVXgONZ6ESdVp2Q5vQtSU8eBOmuwt7Bt8GFjwpXCAkJflyy0qBhCduOjcbWK45Za6YwCapmYD1bXPv3cHkfdqVC-ozxtPrjWmUPtZvfa6bacoK9Ti9j-5FmyHbC1g_QLd0_xDdXakk-Qi9dqhnN6CeBdQzQD2LqGcR9exH1B-jT28PTt8c4tgeA0uw8y6w4KXmrOWMS9oxrjQ1pVKmIgZMbCncCSMly2VBTF4b3bFasw4odmUKYaSkT9BGP-v1U5RJwIaqDshMYUrRko5pQVqmS6M6xdpiC9FRGo2MteNdC5PzZgwSnDZBho2TYRNkuIVwGjWE2im_ub8eBd1E_hd4XQO68cuRz_555Da6s1Tt52gDZK5foNvy24VdzF9GJfoOQ6OCNQ |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deep+frequency-recurrent+priors+for+inverse+imaging+reconstruction&rft.jtitle=Signal+processing&rft.au=He%2C+Zhuonan&rft.au=Hong%2C+Kai&rft.au=Zhou%2C+Jinjie&rft.au=Liang%2C+Dong&rft.date=2022-01-01&rft.pub=Elsevier+B.V&rft.issn=0165-1684&rft.eissn=1872-7557&rft.volume=190&rft_id=info:doi/10.1016%2Fj.sigpro.2021.108320&rft.externalDocID=S0165168421003571 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0165-1684&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0165-1684&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0165-1684&client=summon |