Improved multi-focus image fusion using online convolutional sparse coding based on sample-dependent dictionary
Multi-focus image fusion merges multiple images captured from different focused regions of a scene to create a fully-focused image. Convolutional sparse coding (CSC) methods are commonly employed for accurate extraction of focused regions, but they often disregard computational costs. To overcome th...
Saved in:
| Published in: | Signal processing. Image communication Vol. 130; p. 117213 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.01.2025
|
| Subjects: | |
| ISSN: | 0923-5965 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Multi-focus image fusion merges multiple images captured from different focused regions of a scene to create a fully-focused image. Convolutional sparse coding (CSC) methods are commonly employed for accurate extraction of focused regions, but they often disregard computational costs. To overcome this, an online convolutional sparse coding (OCSC) technique was introduced, but its performance is still limited by the number of filters used, affecting overall performance negatively. To address these limitations, a novel approach called Sample-Dependent Dictionary-based Online Convolutional Sparse Coding (SCSC) was proposed. SCSC enables the utilization of additional filters while maintaining low time and space complexity for processing high-dimensional or large data. Leveraging the computational efficiency and effective global feature extraction of SCSC, we propose a novel method for multi-focus image fusion. Our method involves a two-layer decomposition of each source image, yielding a base layer capturing the predominant features and a detail layer containing finer details. The amalgamation of the fused base and detail layers culminates in the reconstruction of the final image. The proposed method significantly mitigates artifacts, preserves fine details at the focus boundary, and demonstrates notable enhancements in both visual quality and objective evaluation of multi-focus image fusion.
•Two-scale image decomposition is used to decompose source images.•A novel fusion rule is proposed to fuse detail layers.•The fusion rules designed are excellent in preservation of detailed information.•Our method significantly reduces artifacts at the focus boundary.•Experimental results demonstrate the superiority and effectiveness of our method. |
|---|---|
| AbstractList | Multi-focus image fusion merges multiple images captured from different focused regions of a scene to create a fully-focused image. Convolutional sparse coding (CSC) methods are commonly employed for accurate extraction of focused regions, but they often disregard computational costs. To overcome this, an online convolutional sparse coding (OCSC) technique was introduced, but its performance is still limited by the number of filters used, affecting overall performance negatively. To address these limitations, a novel approach called Sample-Dependent Dictionary-based Online Convolutional Sparse Coding (SCSC) was proposed. SCSC enables the utilization of additional filters while maintaining low time and space complexity for processing high-dimensional or large data. Leveraging the computational efficiency and effective global feature extraction of SCSC, we propose a novel method for multi-focus image fusion. Our method involves a two-layer decomposition of each source image, yielding a base layer capturing the predominant features and a detail layer containing finer details. The amalgamation of the fused base and detail layers culminates in the reconstruction of the final image. The proposed method significantly mitigates artifacts, preserves fine details at the focus boundary, and demonstrates notable enhancements in both visual quality and objective evaluation of multi-focus image fusion.
•Two-scale image decomposition is used to decompose source images.•A novel fusion rule is proposed to fuse detail layers.•The fusion rules designed are excellent in preservation of detailed information.•Our method significantly reduces artifacts at the focus boundary.•Experimental results demonstrate the superiority and effectiveness of our method. |
| ArticleNumber | 117213 |
| Author | He, Sidi Zhang, Chengfang Li, Haoyue Feng, Ziliang |
| Author_xml | – sequence: 1 givenname: Sidi surname: He fullname: He, Sidi organization: College of Computer Science, Sichuan University, No. 24, Section 1, South 1st ring road, cheng du, 610039, China – sequence: 2 givenname: Chengfang surname: Zhang fullname: Zhang, Chengfang email: chengfangzhang@scpolicec.edu.cn organization: Intelligent Policing Key Laboratory of Sichuan Province, Sichuan Police College, 186 longtouguan Road, lu zhou, 646000, China – sequence: 3 givenname: Haoyue surname: Li fullname: Li, Haoyue organization: College of Computer Science, Sichuan University, No. 24, Section 1, South 1st ring road, cheng du, 610039, China – sequence: 4 givenname: Ziliang surname: Feng fullname: Feng, Ziliang organization: College of Computer Science, Sichuan University, No. 24, Section 1, South 1st ring road, cheng du, 610039, China |
| BookMark | eNp9kMtqwzAQRbVIoUnaL-hGP2BXj8iOFl2U0Ecg0E27FrI0Dgq2ZCQ7kL-vnHTdzQzMzLncuSu08MEDQk-UlJTQ6vlUul4foWSEbUpKa0b5Ai2JZLwQshL3aJXSiZC8JXKJwr4fYjiDxf3Uja5og5kSvirgdkoueJyrP-LgO-cBm-DPoZvGvNAdToOOaR7a-aTRKetkIul-6KCwMIC34EdsnbkS8fKA7lrdJXj862v08_72vfssDl8f-93roTBM8LGgVssKgElTC77dCEEaIaFhLc--a8K2m4q3mloioKmb2tQEKGMgm0qCbrTha8RvuiaGlCK0aoj5q3hRlKg5J3VS1y_VnJO65ZSplxsF2drZQVTJOPAGrItgRmWD-5f_BVFTeLc |
| Cites_doi | 10.1016/j.inffus.2014.10.004 10.1016/j.inffus.2022.11.010 10.1364/AO.57.010092 10.1007/s11042-020-08945-z 10.1109/TIP.2003.819861 10.1117/1.JEI.30.5.053016 10.1007/s00138-022-01326-6 10.1007/s00138-004-0171-4 10.1016/j.inffus.2020.08.022 10.1016/j.isprsjprs.2024.04.016 10.1007/s11042-020-08670-7 10.1109/CVPR.2015.7299149 10.1117/1.JEI.23.5.053011 10.1016/j.patrec.2006.05.004 10.1016/j.inffus.2014.09.004 10.1016/j.inffus.2007.04.003 10.1117/1.JEI.29.3.033016 10.1109/TIP.2017.2658954 10.1109/TPAMI.2020.3012548 10.1109/97.995823 10.1016/j.sigpro.2020.107513 10.1016/j.image.2019.06.002 10.1109/TIP.2020.2977573 10.1016/j.inffus.2012.01.007 10.1007/s00138-022-01322-w 10.1016/j.inffus.2014.05.004 10.1364/AO.55.010352 10.1016/0167-8655(89)90003-2 10.1007/s00138-013-0502-4 10.1109/LSP.2016.2618776 10.1117/1.JEI.28.2.023005 10.1049/el.2016.1451 10.1016/j.patrec.2006.09.005 10.1016/j.inffus.2011.07.001 10.1109/ACCESS.2020.2971137 10.1109/ACCESS.2019.2909591 10.1038/381607a0 10.3390/e23020247 10.1117/1.JEI.28.5.053001 10.1016/j.inffus.2016.12.001 10.1049/el:20000267 10.1016/j.sigpro.2021.108062 10.1016/j.image.2021.116521 10.1016/j.inffus.2018.01.015 10.1109/TIP.2015.2495260 10.1109/26.477498 10.1016/j.inffus.2011.08.002 10.1007/s11042-020-08661-8 10.1109/TIP.2018.2842152 10.1007/s00138-022-01345-3 10.1016/j.neucom.2019.01.048 10.1109/TIM.2009.2026612 10.1016/j.infrared.2016.07.016 10.1016/j.jvcir.2017.02.006 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier B.V. |
| Copyright_xml | – notice: 2024 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.image.2024.117213 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences Engineering Computer Science |
| ExternalDocumentID | 10_1016_j_image_2024_117213 S0923596524001140 |
| GroupedDBID | --K --M .DC .~1 0R~ 123 1B1 1~. 1~5 4.4 457 4G. 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXKI AAXUO AAYFN ABBOA ABDPE ABFNM ABMAC ABWVN ABXDB ACDAQ ACGFS ACNNM ACRLP ACRPL ACZNC ADBBV ADEZE ADJOM ADMUD ADNMO ADTZH AEBSH AECPX AEIPS AEKER AFJKZ AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 EBS EFJIC EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HLZ HVGLF HZ~ IHE J1W JJJVA KOM LG9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SES SEW SPC SPCBC SST SSV SSZ T5K WUQ XPP ZMT ~G- 9DU AATTM AAYWO AAYXX ACLOT AGQPQ AIIUN ANKPU APXCP CITATION EFKBS EFLBG ~HD |
| ID | FETCH-LOGICAL-c253t-1da96ee29c75384550b59eb2f30247028463fa1d05eb7b7c70e122e9b69eabac3 |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001321933500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0923-5965 |
| IngestDate | Sat Nov 29 05:09:01 EST 2025 Thu Jan 23 08:30:51 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Detail preservation Sparse representation Multi-focus image fusion Online convolutional sparse coding CBPDN Sample-dependent dictionary |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c253t-1da96ee29c75384550b59eb2f30247028463fa1d05eb7b7c70e122e9b69eabac3 |
| ParticipantIDs | crossref_primary_10_1016_j_image_2024_117213 elsevier_sciencedirect_doi_10_1016_j_image_2024_117213 |
| PublicationCentury | 2000 |
| PublicationDate | January 2025 2025-01-00 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – month: 01 year: 2025 text: January 2025 |
| PublicationDecade | 2020 |
| PublicationTitle | Signal processing. Image communication |
| PublicationYear | 2025 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Wang, Ma (b13) 2008; 9 Han, Cai, Cao, Xu (b64) 2013; 14 Aymaz, Köse, Aymaz (b23) 2020; 79 Xydeas, Petrovic (b60) 2000; 36 Wu, Mei (b35) 2022; 33 Ma, Zhou, Wang, Miao, Zong (b50) 2019; 335 Tan, Zhou, Rong, Qian, Yu (b53) 2018; 57 Zhou, Li, Li, Wang, Tan (b49) 2019; 7 Zhang, Fu, Li, Zou (b3) 2013; 52 Liu, Liu, Wang (b33) 2015; 24 Xu, Ma, Jiang, Guo, Ling (b40) 2020; 44 Li, Chen, Zhan, Zhang, Saruta, Terata (b16) 2019; 28 Zhang, Le, Shao, Xu, Ma (b37) 2021; 66 Ma, Hu, Liu, Fang, Xu (b34) 2019; 78 Jiang, Fan, Li (b39) 2022; 33 Nejati, Samavi, Shirani (b46) 2015; 25 Zhang, Cui, Wang, Wang (b19) 2020; 29 Zhong, Zhang, Liu, Zhang, Mo, Zhang, Hu, Chen, Qi (b6) 2023 Ilyas, Farid, Khan, Grzegorzek (b54) 2021; 23 Li, Kang, Hu, Yang (b14) 2013; 14 Wang, Bovik (b61) 2002; 9 Zhang (b10) 2021; 30 Luo, Zhang, Wu (b22) 2014; 23 Liu, Chen, Ward, Wang (b8) 2016; 23 Amin-Naji, Aghagolzadeh (b48) 2018; 6 Zhang, Cao, Zhu, Chen (b24) 2019; 28 Li, Chai, Li (b30) 2013; 24 De, Chanda (b17) 2013; 14 Liu, Chen, Peng, Wang (b52) 2017; 36 Yang, Li (b2) 2009; 59 Wang, Yao, Kwok (b12) 2018 Chew (b41) 1999 Gong, Sbalzarini (b56) 2017; 26 Zhang, Li, Tan, Li (b7) 2024; 211 Yao, Kwok, Gao, Chen, Liu (b45) 2016 Luo, Zhang, Zhang, Wu (b59) 2017; 45 Eskicioglu, Fisher (b62) 1995; 43 Xu, Fan, Zhang, Le, Huang (b38) 2020; 8 F. Heide, W. Heidrich, G. Wetzstein, Fast and flexible convolutional sparse coding, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 5135–5143. Zhou (b63) 2004; 13 Wang, Ma, Liu, Li, Liu (b9) 2021; 99 Ch, Riaz, Iltaf, Ghafoor, Ali (b27) 2020; 79 Wang, Wu, Yu, Zhao (b5) 2022; 33 Piella, Heijmans (b57) 2003 Li, Cai, Tan (b21) 2006; 27 Li, Zhou, Tan, Chen, Zuo (b51) 2021; 184 Toet (b25) 1989; 9 Xu, Wang, Wu, Qian (b26) 2016; 78 Xie, Hu, Wang, Zhang, Qin (b29) 2022 Liu, Liu, Wang (b15) 2015; 23 Huang, Jing (b18) 2007; 28 Aymaz, Köse (b31) 2019; 45 Duan, Chen, Chen (b20) 2016; 55 Ma, Xu, Jiang, Mei, Zhang (b58) 2020; 29 Wen, Yang, Celik, Sushkova, Albertini (b36) 2020; 79 Wang, Yao, Kwok, Ni (b11) 2018; 27 Bin, Jiaxiong (b28) 2005; 16 Guo, Cao, Liu (b32) 2020; 171 Zeiler, Krishnan, Taylor, Fergus (b42) 2010 Olshausen, Field (b1) 1996; 381 Cheng, Xu, Wu (b55) 2023; 92 Li, Wu, Durrani (b47) 2018 Mi, Shang, Zhou, Wang (b4) 2016; 52 Wohlberg (b43) 2015; 25 Wang (10.1016/j.image.2024.117213_b11) 2018; 27 Xydeas (10.1016/j.image.2024.117213_b60) 2000; 36 Wang (10.1016/j.image.2024.117213_b5) 2022; 33 Ch (10.1016/j.image.2024.117213_b27) 2020; 79 Tan (10.1016/j.image.2024.117213_b53) 2018; 57 Ma (10.1016/j.image.2024.117213_b58) 2020; 29 Li (10.1016/j.image.2024.117213_b47) 2018 Wang (10.1016/j.image.2024.117213_b61) 2002; 9 Ma (10.1016/j.image.2024.117213_b50) 2019; 335 Zhou (10.1016/j.image.2024.117213_b49) 2019; 7 De (10.1016/j.image.2024.117213_b17) 2013; 14 Yao (10.1016/j.image.2024.117213_b45) 2016 Amin-Naji (10.1016/j.image.2024.117213_b48) 2018; 6 Aymaz (10.1016/j.image.2024.117213_b23) 2020; 79 Mi (10.1016/j.image.2024.117213_b4) 2016; 52 Ma (10.1016/j.image.2024.117213_b34) 2019; 78 Zhang (10.1016/j.image.2024.117213_b37) 2021; 66 Zhang (10.1016/j.image.2024.117213_b24) 2019; 28 Li (10.1016/j.image.2024.117213_b30) 2013; 24 Wang (10.1016/j.image.2024.117213_b12) 2018 Jiang (10.1016/j.image.2024.117213_b39) 2022; 33 Yang (10.1016/j.image.2024.117213_b2) 2009; 59 Olshausen (10.1016/j.image.2024.117213_b1) 1996; 381 Zhang (10.1016/j.image.2024.117213_b19) 2020; 29 Eskicioglu (10.1016/j.image.2024.117213_b62) 1995; 43 Li (10.1016/j.image.2024.117213_b21) 2006; 27 Chew (10.1016/j.image.2024.117213_b41) 1999 Zeiler (10.1016/j.image.2024.117213_b42) 2010 Wang (10.1016/j.image.2024.117213_b9) 2021; 99 Wen (10.1016/j.image.2024.117213_b36) 2020; 79 Zhong (10.1016/j.image.2024.117213_b6) 2023 Wu (10.1016/j.image.2024.117213_b35) 2022; 33 Li (10.1016/j.image.2024.117213_b51) 2021; 184 Bin (10.1016/j.image.2024.117213_b28) 2005; 16 Liu (10.1016/j.image.2024.117213_b33) 2015; 24 Piella (10.1016/j.image.2024.117213_b57) 2003 Xu (10.1016/j.image.2024.117213_b38) 2020; 8 Toet (10.1016/j.image.2024.117213_b25) 1989; 9 Liu (10.1016/j.image.2024.117213_b8) 2016; 23 Cheng (10.1016/j.image.2024.117213_b55) 2023; 92 Liu (10.1016/j.image.2024.117213_b52) 2017; 36 Zhou (10.1016/j.image.2024.117213_b63) 2004; 13 Li (10.1016/j.image.2024.117213_b14) 2013; 14 Nejati (10.1016/j.image.2024.117213_b46) 2015; 25 Luo (10.1016/j.image.2024.117213_b59) 2017; 45 Gong (10.1016/j.image.2024.117213_b56) 2017; 26 Wohlberg (10.1016/j.image.2024.117213_b43) 2015; 25 Xu (10.1016/j.image.2024.117213_b40) 2020; 44 Luo (10.1016/j.image.2024.117213_b22) 2014; 23 Zhang (10.1016/j.image.2024.117213_b10) 2021; 30 Wang (10.1016/j.image.2024.117213_b13) 2008; 9 Li (10.1016/j.image.2024.117213_b16) 2019; 28 Duan (10.1016/j.image.2024.117213_b20) 2016; 55 Han (10.1016/j.image.2024.117213_b64) 2013; 14 Liu (10.1016/j.image.2024.117213_b15) 2015; 23 Xu (10.1016/j.image.2024.117213_b26) 2016; 78 10.1016/j.image.2024.117213_b44 Zhang (10.1016/j.image.2024.117213_b3) 2013; 52 Guo (10.1016/j.image.2024.117213_b32) 2020; 171 Ilyas (10.1016/j.image.2024.117213_b54) 2021; 23 Zhang (10.1016/j.image.2024.117213_b7) 2024; 211 Huang (10.1016/j.image.2024.117213_b18) 2007; 28 Aymaz (10.1016/j.image.2024.117213_b31) 2019; 45 Xie (10.1016/j.image.2024.117213_b29) 2022 |
| References_xml | – volume: 43 start-page: 2959 year: 1995 end-page: 2965 ident: b62 article-title: Image quality measures and their performance publication-title: IEEE Trans. Commun. – volume: 78 start-page: 133 year: 2016 end-page: 146 ident: b26 article-title: Infrared and multi-type images fusion algorithm based on contrast pyramid transform publication-title: Infrared Phys. Technol. – volume: 33 start-page: 1 year: 2022 end-page: 14 ident: b35 article-title: Multi-focus image fusion based on unsupervised learning publication-title: Mach. Vis. Appl. – volume: 23 start-page: 1882 year: 2016 end-page: 1886 ident: b8 article-title: Image fusion with convolutional sparse representation publication-title: IEEE Signal Process. Lett. – volume: 25 start-page: 301 year: 2015 end-page: 315 ident: b43 article-title: Efficient algorithms for convolutional sparse representations publication-title: IEEE Trans. Image Process. – volume: 79 start-page: 13311 year: 2020 end-page: 13350 ident: b23 article-title: Multi-focus image fusion for different datasets with super-resolution using gradient-based new fusion rule publication-title: Multimedia Tools Appl. – volume: 92 start-page: 80 year: 2023 end-page: 92 ident: b55 article-title: MUFusion: A general unsupervised image fusion network based on memory unit publication-title: Inf. Fusion – volume: 9 start-page: 81 year: 2002 end-page: 84 ident: b61 article-title: A universal image quality index publication-title: IEEE Signal Process. Lett. – volume: 28 start-page: 493 year: 2007 end-page: 500 ident: b18 article-title: Evaluation of focus measures in multi-focus image fusion publication-title: Pattern Recognit. Lett. – volume: 24 start-page: 147 year: 2015 end-page: 164 ident: b33 article-title: A general framework for image fusion based on multi-scale transform and sparse representation publication-title: Inf. Fusion – volume: 28 year: 2019 ident: b24 article-title: Two-scale decomposition and global sparse features for fusion of multifocus images publication-title: J. Electron. Imaging – start-page: 1 year: 2022 end-page: 17 ident: b29 article-title: Novel and fast EMD-based image fusion via morphological filter publication-title: Vis. Comput. – start-page: 2528 year: 2010 end-page: 2535 ident: b42 article-title: Deconvolutional networks publication-title: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition – volume: 36 start-page: 191 year: 2017 end-page: 207 ident: b52 article-title: Multi-focus image fusion with a deep convolutional neural network publication-title: Inf. Fusion – volume: 14 start-page: 127 year: 2013 end-page: 135 ident: b64 article-title: A new image fusion performance metric based on visual information fidelity publication-title: Inf. Fusion – volume: 52 year: 2013 ident: b3 article-title: Dictionary learning method for joint sparse representation-based image fusion publication-title: Opt. Eng., Bellingham – volume: 33 start-page: 1 year: 2022 end-page: 18 ident: b39 article-title: Multi-level receptive field feature reuse for multi-focus image fusion publication-title: Mach. Vis. Appl. – volume: 52 start-page: 1528 year: 2016 end-page: 1529 ident: b4 article-title: Image fusion-based video deraining using sparse representation publication-title: Electron. Lett. – year: 2023 ident: b6 article-title: Unsupervised fusion of misaligned PAT and MRI images via mutually reinforcing cross-modality image generation and registration publication-title: IEEE Trans. Med. Imaging – start-page: 5209 year: 2018 end-page: 5218 ident: b12 article-title: Online convolutional sparse coding with sample-dependent dictionary publication-title: International Conference on Machine Learning – volume: 27 start-page: 1948 year: 2006 end-page: 1956 ident: b21 article-title: A region-based multi-sensor image fusion scheme using pulse-coupled neural network publication-title: Pattern Recognit. Lett. – volume: 16 start-page: 189 year: 2005 end-page: 196 ident: b28 article-title: Image fusion method based on nonseparable wavelets publication-title: Mach. Vis. Appl. – year: 1999 ident: b41 publication-title: Waves and Fields in Inhomogenous Media – volume: 8 start-page: 26316 year: 2020 end-page: 26327 ident: b38 article-title: A deep model for multi-focus image fusion based on gradients and connected regions publication-title: IEEE Access – volume: 381 start-page: 607 year: 1996 end-page: 609 ident: b1 article-title: Emergence of simple-cell receptive field properties by learning a sparse code for natural images publication-title: Nature – volume: 44 start-page: 502 year: 2020 end-page: 518 ident: b40 article-title: U2Fusion: A unified unsupervised image fusion network publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 30 year: 2021 ident: b10 article-title: Multifocus image fusion using convolutional dictionary learning with adaptive contrast enhancement publication-title: J. Electron. Imaging – volume: 45 start-page: 46 year: 2017 end-page: 61 ident: b59 article-title: Multi-focus image fusion using HOSVD and edge intensity publication-title: J. Vis. Commun. Image Represent. – volume: 25 start-page: 72 year: 2015 end-page: 84 ident: b46 article-title: Multi-focus image fusion using dictionary-based sparse representation publication-title: Inf. Fusion – volume: 23 start-page: 247 year: 2021 ident: b54 article-title: Exploiting superpixels for multi-focus image fusion publication-title: Entropy – volume: 45 start-page: 113 year: 2019 end-page: 127 ident: b31 article-title: A novel image decomposition-based hybrid technique with super-resolution method for multi-focus image fusion publication-title: Inf. Fusion – volume: 24 start-page: 1167 year: 2013 end-page: 1181 ident: b30 article-title: A new fusion scheme for multifocus images based on focused pixels detection publication-title: Mach. Vis. Appl. – volume: 23 start-page: 139 year: 2015 end-page: 155 ident: b15 article-title: Multi-focus image fusion with dense SIFT publication-title: Inf. Fusion – volume: 29 start-page: 4980 year: 2020 end-page: 4995 ident: b58 article-title: DDcGAN: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion publication-title: IEEE Trans. Image Process. – volume: 79 start-page: 34531 year: 2020 end-page: 34543 ident: b36 article-title: Multifocus image fusion using convolutional neural network publication-title: Multimedia Tools Appl. – volume: 26 start-page: 1786 year: 2017 end-page: 1798 ident: b56 article-title: Curvature filters efficiently reduce certain variational energies publication-title: IEEE Trans. Image Process. – volume: 79 start-page: 12817 year: 2020 end-page: 12828 ident: b27 article-title: A multifocus image fusion using highlevel DWT components and guided filter publication-title: Multimedia Tools Appl. – volume: 7 start-page: 50780 year: 2019 end-page: 50796 ident: b49 article-title: Multifocus image fusion based on fast guided filter and focus pixels detection publication-title: IEEE Access – volume: 13 start-page: 600 year: 2004 end-page: 613 ident: b63 article-title: Image quality assessment: from error measurement to structural similarity publication-title: IEEE Trans. Image Process. – volume: 66 start-page: 40 year: 2021 end-page: 53 ident: b37 article-title: MFF-GAN: An unsupervised generative adversarial network with adaptive and gradient joint constraints for multi-focus image fusion publication-title: Inf. Fusion – volume: 57 start-page: 10092 year: 2018 end-page: 10101 ident: b53 article-title: Fusion of multi-focus images via a Gaussian curvature filter and synthetic focusing degree criterion publication-title: Appl. Opt. – volume: 184 year: 2021 ident: b51 article-title: Multi-focus image fusion based on nonsubsampled contourlet transform and residual removal publication-title: Signal Process. – volume: 211 start-page: 281 year: 2024 end-page: 297 ident: b7 article-title: Enhanced wavelet based spatiotemporal fusion networks using cross-paired remote sensing images publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 59 start-page: 884 year: 2009 end-page: 892 ident: b2 article-title: Multifocus image fusion and restoration with sparse representation publication-title: IEEE Trans. Instrum. Meas. – volume: 335 start-page: 9 year: 2019 end-page: 20 ident: b50 article-title: Multi-focus image fusion using boosted random walks-based algorithm with two-scale focus maps publication-title: Neurocomputing – volume: 55 start-page: 10352 year: 2016 end-page: 10362 ident: b20 article-title: Multifocus image fusion using superpixel segmentation and superpixel-based mean filtering publication-title: Appl. Opt. – year: 2018 ident: b47 article-title: Multi-focus noisy image fusion using low-rank representation – volume: 14 start-page: 147 year: 2013 end-page: 162 ident: b14 article-title: Image matting for fusion of multi-focus images in dynamic scenes publication-title: Inf. Fusion – volume: 36 start-page: 308 year: 2000 end-page: 309 ident: b60 article-title: Objective image fusion performance measure publication-title: Electron. Lett. – volume: 14 start-page: 136 year: 2013 end-page: 146 ident: b17 article-title: Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure publication-title: Inf. Fusion – volume: 9 start-page: 245 year: 1989 end-page: 253 ident: b25 article-title: Image fusion by a ratio of low-pass pyramid publication-title: Pattern Recognit. Lett. – volume: 28 year: 2019 ident: b16 article-title: Multifocus image fusion using structure-preserving filter publication-title: J. Electron. Imaging – year: 2016 ident: b45 article-title: Efficient inexact proximal gradient algorithm for nonconvex problems – volume: 29 year: 2020 ident: b19 article-title: Multifocus image fusion and depth reconstruction publication-title: J. Electron. Imaging – volume: 9 start-page: 176 year: 2008 end-page: 185 ident: b13 article-title: Medical image fusion using m-PCNN publication-title: Inf. Fusion – volume: 78 start-page: 125 year: 2019 end-page: 134 ident: b34 article-title: Multi-focus image fusion based on joint sparse representation and optimum theory publication-title: Signal Process., Image Commun. – volume: 6 start-page: 233 year: 2018 end-page: 250 ident: b48 article-title: Multi-focus image fusion in DCT domain using variance and energy of Laplacian and correlation coefficient for visual sensor networks publication-title: J. AI Data Min. – volume: 33 start-page: 1 year: 2022 end-page: 16 ident: b5 article-title: Joint patch clustering-based adaptive dictionary and sparse representation for multi-modality image fusion publication-title: Mach. Vis. Appl. – volume: 23 year: 2014 ident: b22 article-title: Adaptive multistrategy image fusion method publication-title: J. Electron. Imaging – volume: 27 start-page: 4850 year: 2018 end-page: 4859 ident: b11 article-title: Scalable online convolutional sparse coding publication-title: IEEE Trans. Image Process. – volume: 171 year: 2020 ident: b32 article-title: Dual-tree biquaternion wavelet transform and its application to color image fusion publication-title: Signal Process. – volume: 99 year: 2021 ident: b9 article-title: Multi-focus image fusion via Joint convolutional analysis and synthesis sparse representation publication-title: Signal Process., Image Commun. – start-page: III year: 2003 end-page: 173 ident: b57 article-title: A new quality metric for image fusion publication-title: Proceedings 2003 International Conference on Image Processing (Cat. No. 03CH37429) – reference: F. Heide, W. Heidrich, G. Wetzstein, Fast and flexible convolutional sparse coding, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 5135–5143. – volume: 25 start-page: 72 year: 2015 ident: 10.1016/j.image.2024.117213_b46 article-title: Multi-focus image fusion using dictionary-based sparse representation publication-title: Inf. Fusion doi: 10.1016/j.inffus.2014.10.004 – volume: 92 start-page: 80 year: 2023 ident: 10.1016/j.image.2024.117213_b55 article-title: MUFusion: A general unsupervised image fusion network based on memory unit publication-title: Inf. Fusion doi: 10.1016/j.inffus.2022.11.010 – volume: 57 start-page: 10092 issue: 35 year: 2018 ident: 10.1016/j.image.2024.117213_b53 article-title: Fusion of multi-focus images via a Gaussian curvature filter and synthetic focusing degree criterion publication-title: Appl. Opt. doi: 10.1364/AO.57.010092 – volume: 79 start-page: 34531 issue: 45 year: 2020 ident: 10.1016/j.image.2024.117213_b36 article-title: Multifocus image fusion using convolutional neural network publication-title: Multimedia Tools Appl. doi: 10.1007/s11042-020-08945-z – volume: 13 start-page: 600 year: 2004 ident: 10.1016/j.image.2024.117213_b63 article-title: Image quality assessment: from error measurement to structural similarity publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2003.819861 – volume: 30 issue: 5 year: 2021 ident: 10.1016/j.image.2024.117213_b10 article-title: Multifocus image fusion using convolutional dictionary learning with adaptive contrast enhancement publication-title: J. Electron. Imaging doi: 10.1117/1.JEI.30.5.053016 – volume: 33 start-page: 1 issue: 5 year: 2022 ident: 10.1016/j.image.2024.117213_b35 article-title: Multi-focus image fusion based on unsupervised learning publication-title: Mach. Vis. Appl. doi: 10.1007/s00138-022-01326-6 – volume: 16 start-page: 189 issue: 3 year: 2005 ident: 10.1016/j.image.2024.117213_b28 article-title: Image fusion method based on nonseparable wavelets publication-title: Mach. Vis. Appl. doi: 10.1007/s00138-004-0171-4 – volume: 66 start-page: 40 year: 2021 ident: 10.1016/j.image.2024.117213_b37 article-title: MFF-GAN: An unsupervised generative adversarial network with adaptive and gradient joint constraints for multi-focus image fusion publication-title: Inf. Fusion doi: 10.1016/j.inffus.2020.08.022 – volume: 211 start-page: 281 year: 2024 ident: 10.1016/j.image.2024.117213_b7 article-title: Enhanced wavelet based spatiotemporal fusion networks using cross-paired remote sensing images publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2024.04.016 – volume: 79 start-page: 13311 issue: 19 year: 2020 ident: 10.1016/j.image.2024.117213_b23 article-title: Multi-focus image fusion for different datasets with super-resolution using gradient-based new fusion rule publication-title: Multimedia Tools Appl. doi: 10.1007/s11042-020-08670-7 – ident: 10.1016/j.image.2024.117213_b44 doi: 10.1109/CVPR.2015.7299149 – volume: 23 issue: 5 year: 2014 ident: 10.1016/j.image.2024.117213_b22 article-title: Adaptive multistrategy image fusion method publication-title: J. Electron. Imaging doi: 10.1117/1.JEI.23.5.053011 – volume: 27 start-page: 1948 issue: 16 year: 2006 ident: 10.1016/j.image.2024.117213_b21 article-title: A region-based multi-sensor image fusion scheme using pulse-coupled neural network publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2006.05.004 – volume: 24 start-page: 147 year: 2015 ident: 10.1016/j.image.2024.117213_b33 article-title: A general framework for image fusion based on multi-scale transform and sparse representation publication-title: Inf. Fusion doi: 10.1016/j.inffus.2014.09.004 – volume: 9 start-page: 176 issue: 2 year: 2008 ident: 10.1016/j.image.2024.117213_b13 article-title: Medical image fusion using m-PCNN publication-title: Inf. Fusion doi: 10.1016/j.inffus.2007.04.003 – volume: 29 issue: 3 year: 2020 ident: 10.1016/j.image.2024.117213_b19 article-title: Multifocus image fusion and depth reconstruction publication-title: J. Electron. Imaging doi: 10.1117/1.JEI.29.3.033016 – volume: 26 start-page: 1786 issue: 4 year: 2017 ident: 10.1016/j.image.2024.117213_b56 article-title: Curvature filters efficiently reduce certain variational energies publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2658954 – volume: 44 start-page: 502 issue: 1 year: 2020 ident: 10.1016/j.image.2024.117213_b40 article-title: U2Fusion: A unified unsupervised image fusion network publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2020.3012548 – volume: 9 start-page: 81 issue: 3 year: 2002 ident: 10.1016/j.image.2024.117213_b61 article-title: A universal image quality index publication-title: IEEE Signal Process. Lett. doi: 10.1109/97.995823 – volume: 171 year: 2020 ident: 10.1016/j.image.2024.117213_b32 article-title: Dual-tree biquaternion wavelet transform and its application to color image fusion publication-title: Signal Process. doi: 10.1016/j.sigpro.2020.107513 – volume: 78 start-page: 125 year: 2019 ident: 10.1016/j.image.2024.117213_b34 article-title: Multi-focus image fusion based on joint sparse representation and optimum theory publication-title: Signal Process., Image Commun. doi: 10.1016/j.image.2019.06.002 – volume: 29 start-page: 4980 year: 2020 ident: 10.1016/j.image.2024.117213_b58 article-title: DDcGAN: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2020.2977573 – volume: 6 start-page: 233 issue: 2 year: 2018 ident: 10.1016/j.image.2024.117213_b48 article-title: Multi-focus image fusion in DCT domain using variance and energy of Laplacian and correlation coefficient for visual sensor networks publication-title: J. AI Data Min. – volume: 14 start-page: 136 issue: 2 year: 2013 ident: 10.1016/j.image.2024.117213_b17 article-title: Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure publication-title: Inf. Fusion doi: 10.1016/j.inffus.2012.01.007 – volume: 33 start-page: 1 issue: 5 year: 2022 ident: 10.1016/j.image.2024.117213_b5 article-title: Joint patch clustering-based adaptive dictionary and sparse representation for multi-modality image fusion publication-title: Mach. Vis. Appl. doi: 10.1007/s00138-022-01322-w – volume: 23 start-page: 139 year: 2015 ident: 10.1016/j.image.2024.117213_b15 article-title: Multi-focus image fusion with dense SIFT publication-title: Inf. Fusion doi: 10.1016/j.inffus.2014.05.004 – volume: 55 start-page: 10352 issue: 36 year: 2016 ident: 10.1016/j.image.2024.117213_b20 article-title: Multifocus image fusion using superpixel segmentation and superpixel-based mean filtering publication-title: Appl. Opt. doi: 10.1364/AO.55.010352 – volume: 9 start-page: 245 issue: 4 year: 1989 ident: 10.1016/j.image.2024.117213_b25 article-title: Image fusion by a ratio of low-pass pyramid publication-title: Pattern Recognit. Lett. doi: 10.1016/0167-8655(89)90003-2 – volume: 24 start-page: 1167 issue: 6 year: 2013 ident: 10.1016/j.image.2024.117213_b30 article-title: A new fusion scheme for multifocus images based on focused pixels detection publication-title: Mach. Vis. Appl. doi: 10.1007/s00138-013-0502-4 – volume: 23 start-page: 1882 issue: 12 year: 2016 ident: 10.1016/j.image.2024.117213_b8 article-title: Image fusion with convolutional sparse representation publication-title: IEEE Signal Process. Lett. doi: 10.1109/LSP.2016.2618776 – volume: 28 issue: 2 year: 2019 ident: 10.1016/j.image.2024.117213_b16 article-title: Multifocus image fusion using structure-preserving filter publication-title: J. Electron. Imaging doi: 10.1117/1.JEI.28.2.023005 – start-page: III year: 2003 ident: 10.1016/j.image.2024.117213_b57 article-title: A new quality metric for image fusion – year: 2018 ident: 10.1016/j.image.2024.117213_b47 – volume: 52 start-page: 1528 issue: 18 year: 2016 ident: 10.1016/j.image.2024.117213_b4 article-title: Image fusion-based video deraining using sparse representation publication-title: Electron. Lett. doi: 10.1049/el.2016.1451 – year: 2023 ident: 10.1016/j.image.2024.117213_b6 article-title: Unsupervised fusion of misaligned PAT and MRI images via mutually reinforcing cross-modality image generation and registration publication-title: IEEE Trans. Med. Imaging – volume: 28 start-page: 493 issue: 4 year: 2007 ident: 10.1016/j.image.2024.117213_b18 article-title: Evaluation of focus measures in multi-focus image fusion publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2006.09.005 – start-page: 2528 year: 2010 ident: 10.1016/j.image.2024.117213_b42 article-title: Deconvolutional networks – volume: 14 start-page: 147 issue: 2 year: 2013 ident: 10.1016/j.image.2024.117213_b14 article-title: Image matting for fusion of multi-focus images in dynamic scenes publication-title: Inf. Fusion doi: 10.1016/j.inffus.2011.07.001 – volume: 8 start-page: 26316 year: 2020 ident: 10.1016/j.image.2024.117213_b38 article-title: A deep model for multi-focus image fusion based on gradients and connected regions publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2971137 – year: 1999 ident: 10.1016/j.image.2024.117213_b41 – year: 2016 ident: 10.1016/j.image.2024.117213_b45 – volume: 7 start-page: 50780 year: 2019 ident: 10.1016/j.image.2024.117213_b49 article-title: Multifocus image fusion based on fast guided filter and focus pixels detection publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2909591 – volume: 381 start-page: 607 issue: 6583 year: 1996 ident: 10.1016/j.image.2024.117213_b1 article-title: Emergence of simple-cell receptive field properties by learning a sparse code for natural images publication-title: Nature doi: 10.1038/381607a0 – volume: 23 start-page: 247 issue: 2 year: 2021 ident: 10.1016/j.image.2024.117213_b54 article-title: Exploiting superpixels for multi-focus image fusion publication-title: Entropy doi: 10.3390/e23020247 – volume: 28 issue: 5 year: 2019 ident: 10.1016/j.image.2024.117213_b24 article-title: Two-scale decomposition and global sparse features for fusion of multifocus images publication-title: J. Electron. Imaging doi: 10.1117/1.JEI.28.5.053001 – volume: 36 start-page: 191 year: 2017 ident: 10.1016/j.image.2024.117213_b52 article-title: Multi-focus image fusion with a deep convolutional neural network publication-title: Inf. Fusion doi: 10.1016/j.inffus.2016.12.001 – volume: 36 start-page: 308 issue: 4 year: 2000 ident: 10.1016/j.image.2024.117213_b60 article-title: Objective image fusion performance measure publication-title: Electron. Lett. doi: 10.1049/el:20000267 – volume: 184 year: 2021 ident: 10.1016/j.image.2024.117213_b51 article-title: Multi-focus image fusion based on nonsubsampled contourlet transform and residual removal publication-title: Signal Process. doi: 10.1016/j.sigpro.2021.108062 – volume: 99 year: 2021 ident: 10.1016/j.image.2024.117213_b9 article-title: Multi-focus image fusion via Joint convolutional analysis and synthesis sparse representation publication-title: Signal Process., Image Commun. doi: 10.1016/j.image.2021.116521 – volume: 45 start-page: 113 year: 2019 ident: 10.1016/j.image.2024.117213_b31 article-title: A novel image decomposition-based hybrid technique with super-resolution method for multi-focus image fusion publication-title: Inf. Fusion doi: 10.1016/j.inffus.2018.01.015 – volume: 25 start-page: 301 issue: 1 year: 2015 ident: 10.1016/j.image.2024.117213_b43 article-title: Efficient algorithms for convolutional sparse representations publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2015.2495260 – volume: 43 start-page: 2959 issue: 12 year: 1995 ident: 10.1016/j.image.2024.117213_b62 article-title: Image quality measures and their performance publication-title: IEEE Trans. Commun. doi: 10.1109/26.477498 – volume: 14 start-page: 127 issue: 2 year: 2013 ident: 10.1016/j.image.2024.117213_b64 article-title: A new image fusion performance metric based on visual information fidelity publication-title: Inf. Fusion doi: 10.1016/j.inffus.2011.08.002 – volume: 79 start-page: 12817 issue: 19 year: 2020 ident: 10.1016/j.image.2024.117213_b27 article-title: A multifocus image fusion using highlevel DWT components and guided filter publication-title: Multimedia Tools Appl. doi: 10.1007/s11042-020-08661-8 – volume: 27 start-page: 4850 issue: 10 year: 2018 ident: 10.1016/j.image.2024.117213_b11 article-title: Scalable online convolutional sparse coding publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2018.2842152 – volume: 33 start-page: 1 issue: 6 year: 2022 ident: 10.1016/j.image.2024.117213_b39 article-title: Multi-level receptive field feature reuse for multi-focus image fusion publication-title: Mach. Vis. Appl. doi: 10.1007/s00138-022-01345-3 – volume: 335 start-page: 9 year: 2019 ident: 10.1016/j.image.2024.117213_b50 article-title: Multi-focus image fusion using boosted random walks-based algorithm with two-scale focus maps publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.01.048 – volume: 59 start-page: 884 issue: 4 year: 2009 ident: 10.1016/j.image.2024.117213_b2 article-title: Multifocus image fusion and restoration with sparse representation publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2009.2026612 – volume: 52 issue: 5 year: 2013 ident: 10.1016/j.image.2024.117213_b3 article-title: Dictionary learning method for joint sparse representation-based image fusion publication-title: Opt. Eng., Bellingham – start-page: 5209 year: 2018 ident: 10.1016/j.image.2024.117213_b12 article-title: Online convolutional sparse coding with sample-dependent dictionary – volume: 78 start-page: 133 year: 2016 ident: 10.1016/j.image.2024.117213_b26 article-title: Infrared and multi-type images fusion algorithm based on contrast pyramid transform publication-title: Infrared Phys. Technol. doi: 10.1016/j.infrared.2016.07.016 – start-page: 1 year: 2022 ident: 10.1016/j.image.2024.117213_b29 article-title: Novel and fast EMD-based image fusion via morphological filter publication-title: Vis. Comput. – volume: 45 start-page: 46 year: 2017 ident: 10.1016/j.image.2024.117213_b59 article-title: Multi-focus image fusion using HOSVD and edge intensity publication-title: J. Vis. Commun. Image Represent. doi: 10.1016/j.jvcir.2017.02.006 |
| SSID | ssj0002409 |
| Score | 2.4066727 |
| Snippet | Multi-focus image fusion merges multiple images captured from different focused regions of a scene to create a fully-focused image. Convolutional sparse coding... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| StartPage | 117213 |
| SubjectTerms | CBPDN Detail preservation Multi-focus image fusion Online convolutional sparse coding Sample-dependent dictionary Sparse representation |
| Title | Improved multi-focus image fusion using online convolutional sparse coding based on sample-dependent dictionary |
| URI | https://dx.doi.org/10.1016/j.image.2024.117213 |
| Volume | 130 |
| WOSCitedRecordID | wos001321933500001&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 issn: 0923-5965 databaseCode: AIEXJ dateStart: 19950201 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0002409 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELaWLQc48Cggyks-cFtcNU4cx8eqKqIIVUgUtOIS2Y6zpILsqrupyh_h9zJ-ZbNtVdEDl2hlZSeW55M983keCL1leWZsxiSRmWIkM4XtBigVMXkOx0uqJHO3598-8ePjYjoVn0ejPzEX5vwnb9vi4kIs_quqYQyUbVNnb6HuXigMwG9QOjxB7fD8J8V7mgDsSBcrSOq57paT5peNzak7y41NOscP-BoZLuw8zMemjizA0bWDLtfFHnGVvU5YSltEmMSOuatJ1bh8CLmZUP2lmVkhC598YFsyTI7ch_UwDWVNvzrqtamaK-z1wQ_TzmoZTlUbL-Sba8v5765HIqy6e_l7Y6ma2ZC_oGzAXwQikqaECd8xot-Tw2WN31UT66em1274nns43XXLCO4-zXbXb2-W17507PXBiDHO7bR0QkorpPRC7qAtygGtY7S1f3Q4_dif8WAH-SqOYe6xnpWLHLwyl-ttnoEdc_IIPQgOCN73wHmMRqbdRg-DM4LDVr-EodjvI45to_uD4pVP0DwCDQ-Aht2ssAcadkDDHmh4A2jYAw17oGEHNHgRXwYaXgPtKfr6_vDk4AMJ7TuIpixdkaSSIjeGCg0ucWGz5xUTRtE6hZXhYNdmeVrLpNpjRnHFNd8zCaVGqFwYqaROn6FxO2_Nc4TBBSw05UaYKsk4M0KDDKkzMK3yDHzyHfQuLnC58FVayhvUuoPyqIQyGJregCwBVjf98cXtvvMS3Vsj_hUar8468xrd1eerZnn2JmDqL-Uko5U |
| 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=Improved+multi-focus+image+fusion+using+online+convolutional+sparse+coding+based+on+sample-dependent+dictionary&rft.jtitle=Signal+processing.+Image+communication&rft.au=He%2C+Sidi&rft.au=Zhang%2C+Chengfang&rft.au=Li%2C+Haoyue&rft.au=Feng%2C+Ziliang&rft.date=2025-01-01&rft.issn=0923-5965&rft.volume=130&rft.spage=117213&rft_id=info:doi/10.1016%2Fj.image.2024.117213&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_image_2024_117213 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0923-5965&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0923-5965&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0923-5965&client=summon |