Improving brain MRI denoising using convolutional AutoEncoder and sparse representations
Magnetic Resonance Imaging (MRI) is an essential tool for diagnosing and monitoring diseases under various conditions. However, noise often degrades image quality, leading to inaccurate diagnoses. To address this issue, a Convolutional AutoEncoder-based Orthogonal Matching Pursuit (CAE-OMP) model is...
Saved in:
| Published in: | Expert systems with applications Vol. 263; p. 125711 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
05.03.2025
|
| Subjects: | |
| ISSN: | 0957-4174 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Magnetic Resonance Imaging (MRI) is an essential tool for diagnosing and monitoring diseases under various conditions. However, noise often degrades image quality, leading to inaccurate diagnoses. To address this issue, a Convolutional AutoEncoder-based Orthogonal Matching Pursuit (CAE-OMP) model is proposed for brain MRI image denoising. In this model, the encoder block extracts relevant features from the image while reducing overfitting, and the OMP algorithm creates a sparse representation to enhance denoising. To improve performance and computational efficiency, the traditional greedy search process in OMP is replaced with the Crossover Boosted Elephant Herd Optimization (CBEHO) algorithm. Rather than searching for atoms, CBEHO optimizes parameter selection, thereby reducing search times and enhancing convergence in the OMP process. Using this optimized sparse representation, the model iteratively improves the original image’s approximation by updating residuals and the support set. The decoder block then reconstructs the denoised image features. The proposed method was tested on multiple datasets, including the RSNA MICCAI PNG dataset, the Brain Tumor Detection MRI (BTD-MRI) dataset, the Brain Tumor Classification MRI (BTC-MRI) Images dataset, and the Brain Tumor Segmentation (BraTS2020) dataset. The results show that the CAE-OMP model achieves Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) values of 0.989 and 47.345 on the BTD-MRI dataset, 0.985 and 46.321 on the RSNA MICCAI PNG dataset, 0.978 and 45.453 on the BTC-MRI dataset, and 0.981 and 46.892 on the BraTS2020 dataset, all evaluated at a 15% noise level. These outcomes indicate that the proposed CAE-OMP model outperforms existing methods, demonstrating superior efficiency for denoising brain MRI images. |
|---|---|
| AbstractList | Magnetic Resonance Imaging (MRI) is an essential tool for diagnosing and monitoring diseases under various conditions. However, noise often degrades image quality, leading to inaccurate diagnoses. To address this issue, a Convolutional AutoEncoder-based Orthogonal Matching Pursuit (CAE-OMP) model is proposed for brain MRI image denoising. In this model, the encoder block extracts relevant features from the image while reducing overfitting, and the OMP algorithm creates a sparse representation to enhance denoising. To improve performance and computational efficiency, the traditional greedy search process in OMP is replaced with the Crossover Boosted Elephant Herd Optimization (CBEHO) algorithm. Rather than searching for atoms, CBEHO optimizes parameter selection, thereby reducing search times and enhancing convergence in the OMP process. Using this optimized sparse representation, the model iteratively improves the original image’s approximation by updating residuals and the support set. The decoder block then reconstructs the denoised image features. The proposed method was tested on multiple datasets, including the RSNA MICCAI PNG dataset, the Brain Tumor Detection MRI (BTD-MRI) dataset, the Brain Tumor Classification MRI (BTC-MRI) Images dataset, and the Brain Tumor Segmentation (BraTS2020) dataset. The results show that the CAE-OMP model achieves Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) values of 0.989 and 47.345 on the BTD-MRI dataset, 0.985 and 46.321 on the RSNA MICCAI PNG dataset, 0.978 and 45.453 on the BTC-MRI dataset, and 0.981 and 46.892 on the BraTS2020 dataset, all evaluated at a 15% noise level. These outcomes indicate that the proposed CAE-OMP model outperforms existing methods, demonstrating superior efficiency for denoising brain MRI images. |
| ArticleNumber | 125711 |
| Author | Krishna Priya, MS Madhan Kumar, K. Velayudham, A |
| Author_xml | – sequence: 1 givenname: A orcidid: 0009-0009-4095-0346 surname: Velayudham fullname: Velayudham, A email: velayudham.a@jit.ac.in organization: Department of Computer Science and Engineering, Jansons Institute of Technology (Autonomous), Coimbatore, Tamilnadu, India – sequence: 2 givenname: K. surname: Madhan Kumar fullname: Madhan Kumar, K. email: principal@petengg.ac.in organization: Electronics And Communication Engineering, PET Engineering College, Vallioor, Tamilnadu, India – sequence: 3 givenname: MS surname: Krishna Priya fullname: Krishna Priya, MS email: krishnapriya.ms@jit.ac.in organization: Department of Artificial Intelligence and Data Science, Jansons Institute of Technology (Autonomous), Coimbatore, Tamilnadu, India |
| BookMark | eNp9kM9KAzEQh3OoYKu-gKd9ga2T7J_sgpdSqhYqgih4C-lkVlK2yZJsK769XdeTh15mYPh9w8w3YxPnHTF2y2HOgZd3uznFLz0XIPI5F4XkfMKmUBcyzbnML9ksxh0AlwByyj7W-y74o3WfyTZo65Ln13ViyHkbh9nht6J3R98eeuudbpPFofcrh95QSLQzSex0iJQE6gJFcr0ecvGaXTS6jXTz16_Y-8PqbfmUbl4e18vFJsUMoE9laQzVCELXUHMBWGDOEbDJmsKICkgXUlRYEjYks3yLdc4NFDlWhuumbLIrJsa9GHyMgRrVBbvX4VtxUIMPtVODDzX4UKOPE1T9g9COd_cnCe159H5E6fTU0VJQES05JGMDYa-Mt-fwH8vZgiw |
| CitedBy_id | crossref_primary_10_1002_ima_70106 crossref_primary_10_1007_s11831_025_10303_x crossref_primary_10_1016_j_eswa_2025_129154 |
| Cites_doi | 10.1016/j.crad.2022.08.127 10.1016/j.compbiomed.2023.107619 10.1016/j.amc.2021.126083 10.1016/j.bspc.2023.105477 10.1016/j.compbiomed.2024.108450 10.1016/j.eswa.2021.114884 10.1007/s11042-019-7459-x 10.1007/s10772-020-09793-w 10.1016/j.patrec.2021.08.031 10.3389/fnins.2020.00728 10.1109/TMI.2022.3220681 10.1016/j.tcs.2021.06.005 10.3389/fnins.2020.577937 10.1016/j.patcog.2023.110176 10.3390/math8091415 10.1016/j.compbiomed.2023.107632 10.1016/j.mri.2020.04.006 10.1504/IJIIDS.2020.109462 10.32604/csse.2023.032508 10.1016/j.sciaf.2023.e01680 10.1016/j.compbiomed.2022.106513 10.1016/j.bspc.2023.104901 10.1016/j.bspc.2021.102844 10.1016/j.chemolab.2022.104639 10.1007/s11042-021-11521-8 10.1016/j.neucom.2024.127799 10.1007/s00500-020-05267-y 10.1007/s42979-022-01591-2 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier Ltd |
| Copyright_xml | – notice: 2024 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.eswa.2024.125711 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| ExternalDocumentID | 10_1016_j_eswa_2024_125711 S0957417424025788 |
| GroupedDBID | --K --M .DC .~1 0R~ 13V 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXKI AAXUO AAYFN ABBOA ABFNM ABMAC ABMVD ABUCO ACDAQ ACGFS ACHRH ACNTT ACRLP ACZNC ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFJKZ AFKWA AFTJW AGHFR AGUBO AGUMN AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV AKRWK ALEQD ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM AXJTR BJAXD BKOJK BLXMC BNSAS CS3 DU5 EBS EFJIC EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W JJJVA KOM MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ SDF SDG SDP SDS SES SEW SPC SPCBC SSB SSD SSL SST SSV SSZ T5K TN5 ~G- 29G 9DU AAAKG AAQXK AATTM AAYWO AAYXX ABJNI ABKBG ABUFD ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EFLBG EJD FEDTE FGOYB G-2 HLZ HVGLF HZ~ LG9 LY1 LY7 M41 R2- SBC SET WUQ XPP ZMT ~HD |
| ID | FETCH-LOGICAL-c300t-76dde9c02a909120c5c41c0cf3f5d280ea5728c6ecfe734bc941d054c8d1af6f3 |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001363746100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0957-4174 |
| IngestDate | Tue Nov 18 21:40:59 EST 2025 Sat Nov 29 03:07:45 EST 2025 Sat Dec 21 15:58:36 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Deep learning Neural Network Noise Autoencoder MRI Crossover Boosted Elephant Herd Optimization Denoising |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-76dde9c02a909120c5c41c0cf3f5d280ea5728c6ecfe734bc941d054c8d1af6f3 |
| ORCID | 0009-0009-4095-0346 |
| ParticipantIDs | crossref_primary_10_1016_j_eswa_2024_125711 crossref_citationtrail_10_1016_j_eswa_2024_125711 elsevier_sciencedirect_doi_10_1016_j_eswa_2024_125711 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-03-05 |
| PublicationDateYYYYMMDD | 2025-03-05 |
| PublicationDate_xml | – month: 03 year: 2025 text: 2025-03-05 day: 05 |
| PublicationDecade | 2020 |
| PublicationTitle | Expert systems with applications |
| PublicationYear | 2025 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Zhu, Pan, Lv, Liu, Li (b0175) 2021; 880 Ali, Qureshi, Bhatti, Sohail, Hijji, Saeed (b0010) 2023; 45 Tajima, Akai, Yasaka, Kunimatsu, Yamashita, Akahane, Yoshioka, Abe, Ohtomo, Kiryu (b0130) 2023; 78 Singh, Kommuri, Kumar, Bajaj (b0110) 2021; 176 Upadhyay, Upadhyay, Shukla (b0135) 2021; 400 Xie, Wu, Ni, He (b0150) 2024; 148 https://www.kaggle.com/datasets/awsaf49/brats2020-training-data. Doi: 10.48550/arXiv.2107.02314. Doi: 10.48550/arXiv.2103.06575. Kala, Deepa (b0080) 2020; 79 Wu, Chen, Xie, Shen, Zeng (b0145) 2023; 85 Juneja, Saini, Kaul, Acharjee, Thakur, Jindal (b0075) 2021; 69 Li, Wang, Gao (b0095) 2024; 174 Yan, Yang, Zhao, Jiao, Yang, Miao (b0155) 2023; 167 Hong, Huang, Yang, Li, Qian, Cai (b0040) 2020; 14 Dhabal, Chakrabarti, Mishra, Venkateswaran (b0030) 2021; 25 Juneja, Rathee, Verma, Bhutani, Baghel, Saini, Jindal (b0070) 2024; 87 https://www.kaggle.com/datasets/jarvisgroot/brain-tumor-classification-mri-images. Amiri Golilarz, Gao, Kumar, Ali, Fu, Li (b0015) 2020; 14 Baid, U., Ghodasara, S., Mohan, S., Bilello, M., Calabrese, E., Colak, E., Farahani, K., Kalpathy-Cramer, J., Kitamura, F.C., Pati, S., & Prevedello, L.M. (2021). The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. https://www.kaggle.com/datasets/abhranta/brain-tumor-detection-mri. Kavitha, Shanmugam, Imoize (b0085) 2023; 20 Li, Zhou, Liang, Liu (b0100) 2020; 71 Ali, Kumar, Patil, Ahmed, Banjar, Daud (b0005) 2022; 229 Li, Lei, Alavi, Wang (b0090) 2020; 8 https://www.kaggle.com/datasets/jonathanbesomi/rsna-miccai-png. Zhao, Yang, Li, Zhang (b0170) 2023; 153 Yu, Guo, Zhang, Zhan, Zhao, Lukasiewicz, Xu (b0160) 2023; 167 Sreelakshmi, Inthiyaz (b0115) 2021; 24 Hu, Tian, Zhang, Zhang (b0065) 2024; 592 Zhang, Huang, Jiang, Xu, Chen, Xu (b0165) 2022; 81 Chung, Lee, Ye (b0025) 2022; 42 Hadri, Laghrib, Oummi (b0035) 2021; 151 Srinivas, Rao (b0120) 2020; 13 Rai, S., Bhatt, J.S., & Patra, S.K. (2021). An unsupervised deep learning framework for medical image denoising. Wu, Hu, Liu (b0140) 2021; 2021 Srinivasan, Gurunathan (b0125) 2023; 4 Chung (10.1016/j.eswa.2024.125711_b0025) 2022; 42 Ali (10.1016/j.eswa.2024.125711_b0010) 2023; 45 Zhao (10.1016/j.eswa.2024.125711_b0170) 2023; 153 10.1016/j.eswa.2024.125711_b0055 Upadhyay (10.1016/j.eswa.2024.125711_b0135) 2021; 400 Hadri (10.1016/j.eswa.2024.125711_b0035) 2021; 151 Xie (10.1016/j.eswa.2024.125711_b0150) 2024; 148 10.1016/j.eswa.2024.125711_b0050 Zhu (10.1016/j.eswa.2024.125711_b0175) 2021; 880 Singh (10.1016/j.eswa.2024.125711_b0110) 2021; 176 Yan (10.1016/j.eswa.2024.125711_b0155) 2023; 167 10.1016/j.eswa.2024.125711_b0105 Kavitha (10.1016/j.eswa.2024.125711_b0085) 2023; 20 Wu (10.1016/j.eswa.2024.125711_b0140) 2021; 2021 Wu (10.1016/j.eswa.2024.125711_b0145) 2023; 85 Hong (10.1016/j.eswa.2024.125711_b0040) 2020; 14 Li (10.1016/j.eswa.2024.125711_b0095) 2024; 174 Yu (10.1016/j.eswa.2024.125711_b0160) 2023; 167 Tajima (10.1016/j.eswa.2024.125711_b0130) 2023; 78 Juneja (10.1016/j.eswa.2024.125711_b0075) 2021; 69 Zhang (10.1016/j.eswa.2024.125711_b0165) 2022; 81 Amiri Golilarz (10.1016/j.eswa.2024.125711_b0015) 2020; 14 10.1016/j.eswa.2024.125711_b0045 Kala (10.1016/j.eswa.2024.125711_b0080) 2020; 79 Li (10.1016/j.eswa.2024.125711_b0090) 2020; 8 10.1016/j.eswa.2024.125711_b0020 Sreelakshmi (10.1016/j.eswa.2024.125711_b0115) 2021; 24 Dhabal (10.1016/j.eswa.2024.125711_b0030) 2021; 25 10.1016/j.eswa.2024.125711_b0060 Ali (10.1016/j.eswa.2024.125711_b0005) 2022; 229 Hu (10.1016/j.eswa.2024.125711_b0065) 2024; 592 Srinivas (10.1016/j.eswa.2024.125711_b0120) 2020; 13 Li (10.1016/j.eswa.2024.125711_b0100) 2020; 71 Srinivasan (10.1016/j.eswa.2024.125711_b0125) 2023; 4 Juneja (10.1016/j.eswa.2024.125711_b0070) 2024; 87 |
| References_xml | – volume: 400 year: 2021 ident: b0135 article-title: Magnetic resonance images denoising using a wavelet solution to laplace equation associated with a new variational model publication-title: Applied Mathematics and Computation – volume: 45 year: 2023 ident: b0010 article-title: De-noising brain MRI images by mixing concatenation and residual learning (MCR) publication-title: Computer Systems Science & Engineering – volume: 229 year: 2022 ident: b0005 article-title: DBP-DeepCNN: Prediction of DNA-binding proteins using wavelet-based denoising and deep learning publication-title: Chemometrics and Intelligent Laboratory Systems – volume: 42 start-page: 922 year: 2022 end-page: 934 ident: b0025 article-title: MR image denoising and super-resolution using regularized reverse diffusion publication-title: IEEE Transactions on Medical Imaging – volume: 20 year: 2023 ident: b0085 article-title: Optimized deep knowledge-based no-reference image quality index for denoised MRI images publication-title: Scientific African – volume: 14 start-page: 728 year: 2020 ident: b0015 article-title: Adaptive wavelet based MRI brain image de-noising publication-title: Frontiers in neuroscience – reference: . Doi: 10.48550/arXiv.2107.02314. – reference: https://www.kaggle.com/datasets/abhranta/brain-tumor-detection-mri. – volume: 8 start-page: 1415 year: 2020 ident: b0090 article-title: Elephant herding optimization: Variants, hybrids, and applications publication-title: Mathematics – volume: 174 year: 2024 ident: b0095 article-title: New non-local mean methods for MRI denoising based on global self-similarity between values publication-title: Computers in Biology and Medicine – volume: 25 start-page: 1941 year: 2021 end-page: 1961 ident: b0030 article-title: An improved image denoising technique using differential evolution-based salp swarm algorithm publication-title: Soft Computing – volume: 167 year: 2023 ident: b0160 article-title: RIRGAN: An end-to-end lightweight multi-task learning method for brain MRI super-resolution and denoising publication-title: Computers in Biology and Medicine – volume: 24 start-page: 529 year: 2021 end-page: 544 ident: b0115 article-title: Fast and denoise feature extraction based ADMF–CNN with GBML framework for MRI brain image publication-title: International Journal of Speech Technology – reference: https://www.kaggle.com/datasets/jarvisgroot/brain-tumor-classification-mri-images. – volume: 71 start-page: 55 year: 2020 end-page: 68 ident: b0100 article-title: MRI denoising using progressively distribution-based neural network publication-title: Magnetic Resonance Imaging – volume: 81 start-page: 41751 year: 2022 end-page: 41763 ident: b0165 article-title: Denoising of brain magnetic resonance images using a MDB network publication-title: Multimedia Tools and Applications – reference: Baid, U., Ghodasara, S., Mohan, S., Bilello, M., Calabrese, E., Colak, E., Farahani, K., Kalpathy-Cramer, J., Kitamura, F.C., Pati, S., & Prevedello, L.M. (2021). The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. – volume: 79 start-page: 15513 year: 2020 end-page: 15530 ident: b0080 article-title: Adaptive fuzzy hexagonal bilateral filter for brain MRI denoising publication-title: Multimedia Tools and Applications – volume: 14 year: 2020 ident: b0040 article-title: FFA-DMRI: A network based on feature fusion and attention mechanism for brain MRI denoising publication-title: Frontiers in Neuroscience – volume: 151 start-page: 302 year: 2021 end-page: 309 ident: b0035 article-title: An optimal variable exponent model for Magnetic Resonance Images denoising publication-title: Pattern Recognition Letters – volume: 153 year: 2023 ident: b0170 article-title: SwinGAN: A dual-domain Swin Transformer-based generative adversarial network for MRI reconstruction publication-title: Computers in Biology and Medicine – volume: 148 year: 2024 ident: b0150 article-title: NODE-ImgNet: A PDE-informed effective and robust model for image denoising publication-title: Pattern Recognition – reference: . Doi: 10.48550/arXiv.2103.06575. – volume: 85 year: 2023 ident: b0145 article-title: Super-resolution of brain MRI images based on denoising diffusion probabilistic model publication-title: Biomedical Signal Processing and Control – volume: 78 start-page: e13 year: 2023 end-page: e21 ident: b0130 article-title: Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images publication-title: Clinical Radiology – volume: 69 year: 2021 ident: b0075 article-title: Denoising of magnetic resonance imaging using bayes shrinkage based fused wavelet transform and autoencoder based deep learning approach publication-title: Biomedical Signal Processing and Control – reference: https://www.kaggle.com/datasets/jonathanbesomi/rsna-miccai-png. – volume: 13 start-page: 393 year: 2020 end-page: 410 ident: b0120 article-title: A novel DeepCNN model for denoising analysis of MRI brain tumour images publication-title: International Journal of Intelligent Information and Database Systems – reference: Rai, S., Bhatt, J.S., & Patra, S.K. (2021). An unsupervised deep learning framework for medical image denoising. – volume: 880 start-page: 97 year: 2021 end-page: 110 ident: b0175 article-title: DESN: An unsupervised MR image denoising network with deep image prior publication-title: Theoretical Computer Science – volume: 4 start-page: 166 year: 2023 ident: b0125 article-title: Enriched model of intuitionistic fuzzy adaptive noise filtering on MR brain image publication-title: SN Computer Science – volume: 2021 start-page: 1 year: 2021 end-page: 18 ident: b0140 article-title: Denoising of 3D brain MR images with parallel residual learning of convolutional neural network using global and local feature extraction publication-title: Computational Intelligence and Neuroscience – volume: 592 year: 2024 ident: b0065 article-title: Efficient image denoising with heterogeneous kernel-based CNN publication-title: Neurocomputing – volume: 167 year: 2023 ident: b0155 article-title: DC-SiamNet: Deep contrastive Siamese network for self-supervised MRI reconstruction publication-title: Computers in Biology and Medicine – volume: 176 year: 2021 ident: b0110 article-title: A new technique for guided filter based image denoising using modified cuckoo search optimization publication-title: Expert Systems with Applications – reference: https://www.kaggle.com/datasets/awsaf49/brats2020-training-data. – volume: 87 year: 2024 ident: b0070 article-title: Denoising of magnetic resonance images of brain tumor using BT-Autonet publication-title: Biomedical Signal Processing and Control – volume: 78 start-page: e13 issue: 1 year: 2023 ident: 10.1016/j.eswa.2024.125711_b0130 article-title: Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images publication-title: Clinical Radiology doi: 10.1016/j.crad.2022.08.127 – volume: 167 year: 2023 ident: 10.1016/j.eswa.2024.125711_b0155 article-title: DC-SiamNet: Deep contrastive Siamese network for self-supervised MRI reconstruction publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2023.107619 – ident: 10.1016/j.eswa.2024.125711_b0105 – volume: 400 year: 2021 ident: 10.1016/j.eswa.2024.125711_b0135 article-title: Magnetic resonance images denoising using a wavelet solution to laplace equation associated with a new variational model publication-title: Applied Mathematics and Computation doi: 10.1016/j.amc.2021.126083 – volume: 87 year: 2024 ident: 10.1016/j.eswa.2024.125711_b0070 article-title: Denoising of magnetic resonance images of brain tumor using BT-Autonet publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2023.105477 – volume: 174 year: 2024 ident: 10.1016/j.eswa.2024.125711_b0095 article-title: New non-local mean methods for MRI denoising based on global self-similarity between values publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2024.108450 – volume: 176 year: 2021 ident: 10.1016/j.eswa.2024.125711_b0110 article-title: A new technique for guided filter based image denoising using modified cuckoo search optimization publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2021.114884 – volume: 79 start-page: 15513 year: 2020 ident: 10.1016/j.eswa.2024.125711_b0080 article-title: Adaptive fuzzy hexagonal bilateral filter for brain MRI denoising publication-title: Multimedia Tools and Applications doi: 10.1007/s11042-019-7459-x – volume: 24 start-page: 529 issue: 2 year: 2021 ident: 10.1016/j.eswa.2024.125711_b0115 article-title: Fast and denoise feature extraction based ADMF–CNN with GBML framework for MRI brain image publication-title: International Journal of Speech Technology doi: 10.1007/s10772-020-09793-w – ident: 10.1016/j.eswa.2024.125711_b0050 – volume: 151 start-page: 302 year: 2021 ident: 10.1016/j.eswa.2024.125711_b0035 article-title: An optimal variable exponent model for Magnetic Resonance Images denoising publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2021.08.031 – volume: 14 start-page: 728 year: 2020 ident: 10.1016/j.eswa.2024.125711_b0015 article-title: Adaptive wavelet based MRI brain image de-noising publication-title: Frontiers in neuroscience doi: 10.3389/fnins.2020.00728 – volume: 42 start-page: 922 issue: 4 year: 2022 ident: 10.1016/j.eswa.2024.125711_b0025 article-title: MR image denoising and super-resolution using regularized reverse diffusion publication-title: IEEE Transactions on Medical Imaging doi: 10.1109/TMI.2022.3220681 – volume: 880 start-page: 97 year: 2021 ident: 10.1016/j.eswa.2024.125711_b0175 article-title: DESN: An unsupervised MR image denoising network with deep image prior publication-title: Theoretical Computer Science doi: 10.1016/j.tcs.2021.06.005 – volume: 14 year: 2020 ident: 10.1016/j.eswa.2024.125711_b0040 article-title: FFA-DMRI: A network based on feature fusion and attention mechanism for brain MRI denoising publication-title: Frontiers in Neuroscience doi: 10.3389/fnins.2020.577937 – volume: 148 year: 2024 ident: 10.1016/j.eswa.2024.125711_b0150 article-title: NODE-ImgNet: A PDE-informed effective and robust model for image denoising publication-title: Pattern Recognition doi: 10.1016/j.patcog.2023.110176 – volume: 2021 start-page: 1 year: 2021 ident: 10.1016/j.eswa.2024.125711_b0140 article-title: Denoising of 3D brain MR images with parallel residual learning of convolutional neural network using global and local feature extraction publication-title: Computational Intelligence and Neuroscience – ident: 10.1016/j.eswa.2024.125711_b0045 – ident: 10.1016/j.eswa.2024.125711_b0020 – volume: 8 start-page: 1415 issue: 9 year: 2020 ident: 10.1016/j.eswa.2024.125711_b0090 article-title: Elephant herding optimization: Variants, hybrids, and applications publication-title: Mathematics doi: 10.3390/math8091415 – volume: 167 year: 2023 ident: 10.1016/j.eswa.2024.125711_b0160 article-title: RIRGAN: An end-to-end lightweight multi-task learning method for brain MRI super-resolution and denoising publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2023.107632 – volume: 71 start-page: 55 year: 2020 ident: 10.1016/j.eswa.2024.125711_b0100 article-title: MRI denoising using progressively distribution-based neural network publication-title: Magnetic Resonance Imaging doi: 10.1016/j.mri.2020.04.006 – volume: 13 start-page: 393 issue: 2–4 year: 2020 ident: 10.1016/j.eswa.2024.125711_b0120 article-title: A novel DeepCNN model for denoising analysis of MRI brain tumour images publication-title: International Journal of Intelligent Information and Database Systems doi: 10.1504/IJIIDS.2020.109462 – volume: 45 issue: 2 year: 2023 ident: 10.1016/j.eswa.2024.125711_b0010 article-title: De-noising brain MRI images by mixing concatenation and residual learning (MCR) publication-title: Computer Systems Science & Engineering doi: 10.32604/csse.2023.032508 – volume: 20 year: 2023 ident: 10.1016/j.eswa.2024.125711_b0085 article-title: Optimized deep knowledge-based no-reference image quality index for denoised MRI images publication-title: Scientific African doi: 10.1016/j.sciaf.2023.e01680 – volume: 153 year: 2023 ident: 10.1016/j.eswa.2024.125711_b0170 article-title: SwinGAN: A dual-domain Swin Transformer-based generative adversarial network for MRI reconstruction publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2022.106513 – volume: 85 year: 2023 ident: 10.1016/j.eswa.2024.125711_b0145 article-title: Super-resolution of brain MRI images based on denoising diffusion probabilistic model publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2023.104901 – volume: 69 year: 2021 ident: 10.1016/j.eswa.2024.125711_b0075 article-title: Denoising of magnetic resonance imaging using bayes shrinkage based fused wavelet transform and autoencoder based deep learning approach publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2021.102844 – volume: 229 year: 2022 ident: 10.1016/j.eswa.2024.125711_b0005 article-title: DBP-DeepCNN: Prediction of DNA-binding proteins using wavelet-based denoising and deep learning publication-title: Chemometrics and Intelligent Laboratory Systems doi: 10.1016/j.chemolab.2022.104639 – ident: 10.1016/j.eswa.2024.125711_b0055 – volume: 81 start-page: 41751 issue: 29 year: 2022 ident: 10.1016/j.eswa.2024.125711_b0165 article-title: Denoising of brain magnetic resonance images using a MDB network publication-title: Multimedia Tools and Applications doi: 10.1007/s11042-021-11521-8 – volume: 592 year: 2024 ident: 10.1016/j.eswa.2024.125711_b0065 article-title: Efficient image denoising with heterogeneous kernel-based CNN publication-title: Neurocomputing doi: 10.1016/j.neucom.2024.127799 – volume: 25 start-page: 1941 issue: 3 year: 2021 ident: 10.1016/j.eswa.2024.125711_b0030 article-title: An improved image denoising technique using differential evolution-based salp swarm algorithm publication-title: Soft Computing doi: 10.1007/s00500-020-05267-y – ident: 10.1016/j.eswa.2024.125711_b0060 – volume: 4 start-page: 166 issue: 2 year: 2023 ident: 10.1016/j.eswa.2024.125711_b0125 article-title: Enriched model of intuitionistic fuzzy adaptive noise filtering on MR brain image publication-title: SN Computer Science doi: 10.1007/s42979-022-01591-2 |
| SSID | ssj0017007 |
| Score | 2.468559 |
| Snippet | Magnetic Resonance Imaging (MRI) is an essential tool for diagnosing and monitoring diseases under various conditions. However, noise often degrades image... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 125711 |
| SubjectTerms | Autoencoder Crossover Boosted Elephant Herd Optimization Deep learning Denoising MRI Neural Network Noise |
| Title | Improving brain MRI denoising using convolutional AutoEncoder and sparse representations |
| URI | https://dx.doi.org/10.1016/j.eswa.2024.125711 |
| Volume | 263 |
| WOSCitedRecordID | wos001363746100001&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: ScienceDirect issn: 0957-4174 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0017007 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1La9wwEBZt0kMvfZemL3TozXiRbcmSj6FsaVoSQknL3oysB-wSvIt3N03-fUeWrH20Dc2hF2OELQvPx6fxeGY-hD7IqoFtWbBUEm5Sqq1OG6tYWpWMN5IqKhTpxSb42ZmYTKrzkFa07OUEeNuK6-tq8V9NDWNgbFc6ewdzx0lhAM7B6HAEs8Pxnwy_CRM0Tv4hOf12kgC5zKd9VGAdqmzbq7AIZ6L1aj5uXXG7T6gEkumWTk5lsalNCkG9WczcM90qtIEeCuS2foUPZvxhLuXNWoda7AiWUwlDbRLTu7-OIu87wftWJufd9EZuxcxDXCJnfWIW2wkw8pRmXoNn4No8sJlnS3CuuKfa34jcxxRmI7P86bpD5XS0uXi3a_bebhZzDIf0tVnt5qjdHLWf4z46zDmrgMYPj0_Gky_xrxMnvrx-WHkosvL5gPsr-bMjs-WcXDxBj8JXBT72aHiK7pn2GXo8KHbgQODP0SSCA_fgwAAOHMGBe3DgHXDgLXBgAAf24MB74HiBvn8aX3z8nAZtjVQVhKxSXsK-VimSywo8xpwopmimiLKFZToXxEjGc6FKo6zhBW1URTMN7r0SOpO2tMVLdNDOW_MK4VJnXIpGKmPBPaWZEJUtdGbhy55QrvURyoYXVavQeN7pn1zWfzfREUriPQvfduXWq9nw_uvgOHqHsAY43XLf6zs95Q16uMH5W3Sw6tbmHXqgrlbTZfc-YOkXGYKSrw |
| 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=Improving+brain+MRI+denoising+using+convolutional+AutoEncoder+and+sparse+representations&rft.jtitle=Expert+systems+with+applications&rft.au=Velayudham%2C+A&rft.au=Madhan+Kumar%2C+K.&rft.au=Krishna+Priya%2C+MS&rft.date=2025-03-05&rft.issn=0957-4174&rft.volume=263&rft.spage=125711&rft_id=info:doi/10.1016%2Fj.eswa.2024.125711&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_eswa_2024_125711 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-4174&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-4174&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-4174&client=summon |