{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:18:36Z","timestamp":1771024716551,"version":"3.50.1"},"reference-count":87,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s00521-023-09314-1","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T12:02:37Z","timestamp":1704715357000},"page":"5447-5469","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["SW\/SE-CNN: semi-wavelet and specific image edge extractor CNN for Gaussian image denoising"],"prefix":"10.1007","volume":"36","author":[{"given":"Shahram","family":"Esteki","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7667-0885","authenticated-orcid":false,"given":"Ahmad R.","family":"Naghsh-Nilchi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"key":"9314_CR1","first-page":"1","volume":"2","author":"RC Gonzalez","year":"2007","unstructured":"Gonzalez RC, Woods RE (2007) Image processing. Digital Image Processing 2:1","journal-title":"Digital Image Processing"},{"issue":"8","key":"9314_CR2","doi-asserted-by":"crossref","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","volume":"16","author":"K Dabov","year":"2007","unstructured":"Dabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans Image Process 16(8):2080\u20132095","journal-title":"IEEE Trans Image Process"},{"key":"9314_CR3","doi-asserted-by":"crossref","unstructured":"Gu S, Zhang L, Zuo W and Feng X (2014) Weighted nuclear norm minimization with application to image denoising. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp 2862\u20132869","DOI":"10.1109\/CVPR.2014.366"},{"key":"9314_CR4","doi-asserted-by":"crossref","unstructured":"Liu P, Zhang H, Zhang K, Lin L and Zuo W (2018) Multi-level wavelet-CNN for image restoration. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2018, pp 773\u2013782","DOI":"10.1109\/CVPRW.2018.00121"},{"key":"9314_CR5","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.neunet.2019.08.022","volume":"121","author":"C Tian","year":"2020","unstructured":"Tian C, Xu Y, Zuo W (2020) Image denoising using deep CNN with batch renormalization. Neural Netw 121:461\u2013473","journal-title":"Neural Netw"},{"issue":"7","key":"9314_CR6","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans Image Process 26(7):3142\u20133155","journal-title":"IEEE Trans Image Process"},{"issue":"9","key":"9314_CR7","doi-asserted-by":"crossref","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"K Zhang","year":"2018","unstructured":"Zhang K, Zuo W, Zhang L (2018) FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans Image Process 27(9):4608\u20134622","journal-title":"IEEE Trans Image Process"},{"key":"9314_CR8","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.inffus.2019.09.003","volume":"55","author":"B Goyal","year":"2020","unstructured":"Goyal B, Dogra A, Agrawal S, Sohi B, Sharma A (2020) Image denoising review: From classical to state-of-the-art approaches. Inf Fusion 55:220\u2013244","journal-title":"Inf Fusion"},{"issue":"5","key":"9314_CR9","doi-asserted-by":"crossref","first-page":"2179","DOI":"10.1007\/s40747-021-00428-4","volume":"7","author":"AE Ilesanmi","year":"2021","unstructured":"Ilesanmi AE, Ilesanmi TO (2021) Methods for image denoising using convolutional neural network: a review. Complex Intell Syst 7(5):2179\u20132198","journal-title":"Complex Intell Syst"},{"issue":"1\u20132","key":"9314_CR10","first-page":"92","volume":"24","author":"H Rekha","year":"2023","unstructured":"Rekha H, Samundiswary P (2023) Image denoising using fast non-local means filter and multi-thresholding with harmony search algorithm for WSN. Int J Adv Intell Paradig 24(1\u20132):92\u2013109","journal-title":"Int J Adv Intell Paradig"},{"issue":"7","key":"9314_CR11","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1109\/TCYB.2013.2278548","volume":"44","author":"L Shao","year":"2013","unstructured":"Shao L, Yan R, Li X, Liu Y (2013) From heuristic optimization to dictionary learning: A review and comprehensive comparison of image denoising algorithms. IEEE Trans Cybern 44(7):1001\u20131013","journal-title":"IEEE Trans Cybern"},{"key":"9314_CR12","doi-asserted-by":"crossref","unstructured":"Benesty J, Chen J and Huang Y (2010) Study of the widely linear Wiener filter for noise reduction. In: 2010 IEEE international conference on acoustics, speech and signal processing, 2010: IEEE, pp. 205\u2013208","DOI":"10.1109\/ICASSP.2010.5496033"},{"issue":"2","key":"9314_CR13","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1109\/JSTARS.2015.2468593","volume":"9","author":"Y Teng","year":"2015","unstructured":"Teng Y, Zhang Y, Chen Y, Ti C (2015) Adaptive morphological filtering method for structural fusion restoration of hyperspectral images. IEEE J Sel Top Appl Earth Obs Remote Sens 9(2):655\u2013667","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"issue":"2","key":"9314_CR14","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1109\/83.277900","volume":"3","author":"RC Hardie","year":"1994","unstructured":"Hardie RC, Barner KE (1994) Rank conditioned rank selection filters for signal restoration. IEEE Trans Image Process 3(2):192\u2013206","journal-title":"IEEE Trans Image Process"},{"key":"9314_CR15","doi-asserted-by":"crossref","unstructured":"Buades A, Coll B and Morel J-M (2005) A non-local algorithm for image denoising. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), 2005, 2:60\u201365","DOI":"10.1109\/CVPR.2005.38"},{"issue":"5","key":"9314_CR16","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1109\/76.780358","volume":"9","author":"Z Xiong","year":"1999","unstructured":"Xiong Z, Ramchandran K, Orchard MT, Zhang Y-Q (1999) A comparative study of DCT-and wavelet-based image coding. IEEE Trans Circuits Syst Video Technol 9(5):692\u2013695","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"9","key":"9314_CR17","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1109\/TIP.2012.2200491","volume":"21","author":"A Fathi","year":"2012","unstructured":"Fathi A, Naghsh-Nilchi AR (2012) Efficient image denoising method based on a new adaptive wavelet packet thresholding function. IEEE Trans Image Process 21(9):3981\u20133990","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"9314_CR18","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1109\/TIP.2002.1014998","volume":"11","author":"J-L Starck","year":"2002","unstructured":"Starck J-L, Cand\u00e8s EJ, Donoho DL (2002) The curvelet transform for image denoising. IEEE Trans Image Process 11(6):670\u2013684","journal-title":"IEEE Trans Image Process"},{"key":"9314_CR19","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.dsp.2016.02.004","volume":"52","author":"Q Huang","year":"2016","unstructured":"Huang Q, Hao B, Chang S (2016) Adaptive digital ridgelet transform and its application in image denoising. Digit Signal Process 52:45\u201354","journal-title":"Digit Signal Process"},{"issue":"7","key":"9314_CR20","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1016\/j.jvcir.2010.04.002","volume":"21","author":"J Xu","year":"2010","unstructured":"Xu J, Yang L, Wu D (2010) Ripplet: a new transform for image processing. J Vis Commun Image Represent 21(7):627\u2013639","journal-title":"J Vis Commun Image Represent"},{"issue":"12","key":"9314_CR21","doi-asserted-by":"crossref","first-page":"3736","DOI":"10.1109\/TIP.2006.881969","volume":"15","author":"M Elad","year":"2006","unstructured":"Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 15(12):3736\u20133745","journal-title":"IEEE Trans Image Process"},{"key":"9314_CR22","doi-asserted-by":"crossref","unstructured":"Elad M and Aharon M (2006) Image denoising via learned dictionaries and sparse representation. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR'06) 1:895\u2013900","DOI":"10.1109\/CVPR.2006.142"},{"issue":"7","key":"9314_CR23","doi-asserted-by":"crossref","first-page":"1438","DOI":"10.1109\/TIP.2009.2018575","volume":"18","author":"P Chatterjee","year":"2009","unstructured":"Chatterjee P, Milanfar P (2009) Clustering-based denoising with locally learned dictionaries. IEEE Trans Image Process 18(7):1438\u20131451","journal-title":"IEEE Trans Image Process"},{"key":"9314_CR24","doi-asserted-by":"crossref","unstructured":"Mairal J, Bach F, Ponce J, Sapiro G and Zisserman A (2009) Non-local sparse models for image restoration. In 2009 IEEE 12th international conference on computer vision, 2009: IEEE, pp. 2272\u20132279","DOI":"10.1109\/ICCV.2009.5459452"},{"key":"9314_CR25","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.neunet.2014.06.007","volume":"57","author":"H-Y Yang","year":"2014","unstructured":"Yang H-Y, Wang X-Y, Niu P-P, Liu Y-C (2014) Image denoising using nonsubsampled shearlet transform and twin support vector machines. Neural Netw 57:152\u2013165","journal-title":"Neural Netw"},{"issue":"12","key":"9314_CR26","doi-asserted-by":"crossref","first-page":"3116","DOI":"10.1109\/TIP.2010.2052820","volume":"19","author":"X Zhu","year":"2010","unstructured":"Zhu X, Milanfar P (2010) Automatic parameter selection for denoising algorithms using a no-reference measure of image content. IEEE Trans Image Process 19(12):3116\u20133132","journal-title":"IEEE Trans Image Process"},{"issue":"8","key":"9314_CR27","doi-asserted-by":"crossref","first-page":"1730","DOI":"10.1109\/LGRS.2015.2422788","volume":"12","author":"M Cao","year":"2015","unstructured":"Cao M, Li S, Wang R, Li N (2015) Interferometric phase denoising by median patch-based locally optimal wiener filter. IEEE Geosci Remote Sens Lett 12(8):1730\u20131734","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"9314_CR28","first-page":"1","volume":"2021","author":"H Wei","year":"2021","unstructured":"Wei H, Zheng W (2021) Image denoising based on improved gaussian mixture model. Sci Program 2021:1\u20138","journal-title":"Sci Program"},{"issue":"2","key":"9314_CR29","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s11760-022-02245-9","volume":"17","author":"T Hua","year":"2023","unstructured":"Hua T, Li Q, Dai K, Zhang X, Zhang H (2023) Image denoising via neighborhood-based multidimensional Gaussian process regression. Signal Image Video Process 17(2):389\u2013397","journal-title":"Signal Image Video Process"},{"issue":"2","key":"9314_CR30","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s11263-007-0052-1","volume":"76","author":"A Buades","year":"2008","unstructured":"Buades A, Coll B, Morel J-M (2008) Nonlocal image and movie denoising. Int J Comput Vision 76(2):123\u2013139","journal-title":"Int J Comput Vision"},{"issue":"4","key":"9314_CR31","doi-asserted-by":"crossref","first-page":"1620","DOI":"10.1109\/TIP.2012.2235847","volume":"22","author":"W Dong","year":"2012","unstructured":"Dong W, Zhang L, Shi G, Li X (2012) Nonlocally centralized sparse representation for image restoration. IEEE Trans Image Process 22(4):1620\u20131630","journal-title":"IEEE Trans Image Process"},{"issue":"1\u20134","key":"9314_CR32","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","volume":"60","author":"LI Rudin","year":"1992","unstructured":"Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Physica D 60(1\u20134):259\u2013268","journal-title":"Physica D"},{"issue":"2","key":"9314_CR33","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1137\/040605412","volume":"4","author":"S Osher","year":"2005","unstructured":"Osher S, Burger M, Goldfarb D, Xu J, Yin W (2005) An iterative regularization method for total variation-based image restoration. Multiscale Model Simul 4(2):460\u2013489","journal-title":"Multiscale Model Simul"},{"key":"9314_CR34","doi-asserted-by":"crossref","unstructured":"Weiss Y and Freeman WT (2007) What makes a good model of natural images? In: 2007 IEEE conference on computer vision and pattern recognition, 2007: IEEE, pp. 1\u20138","DOI":"10.1109\/CVPR.2007.383092"},{"issue":"2","key":"9314_CR35","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s11263-008-0197-6","volume":"82","author":"S Roth","year":"2009","unstructured":"Roth S, Black MJ (2009) Fields of experts. Int J Comput Vis 82(2):205","journal-title":"Int J Comput Vis"},{"key":"9314_CR36","volume-title":"Markov random field modeling in image analysis","author":"SZ Li","year":"2009","unstructured":"Li SZ (2009) Markov random field modeling in image analysis. Springer"},{"key":"9314_CR37","doi-asserted-by":"crossref","unstructured":"Lan X, Roth S, Huttenlocher D and Black MJ (2006) Efficient belief propagation with learned higher-order Markov random fields. In: European conference on computer vision, Springer, pp. 269\u2013282","DOI":"10.1007\/11744047_21"},{"issue":"3","key":"9314_CR38","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1587\/transinf.2021EDP7172","volume":"105","author":"Y Monma","year":"2022","unstructured":"Monma Y, Aro K, Yasuda M (2022) Hierarchical Gaussian Markov random field for image denoising. IEICE Trans Inf Syst 105(3):689\u2013699","journal-title":"IEICE Trans Inf Syst"},{"issue":"3","key":"9314_CR39","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/s10589-013-9576-1","volume":"56","author":"C Li","year":"2013","unstructured":"Li C, Yin W, Jiang H, Zhang Y (2013) An efficient augmented Lagrangian method with applications to total variation minimization. Comput Optim Appl 56(3):507\u2013530","journal-title":"Comput Optim Appl"},{"key":"9314_CR40","doi-asserted-by":"crossref","unstructured":"Schmidt U and Roth S (2014) Shrinkage fields for effective image restoration. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 2774\u20132781","DOI":"10.1109\/CVPR.2014.349"},{"issue":"6","key":"9314_CR41","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1109\/TPAMI.2016.2596743","volume":"39","author":"Y Chen","year":"2016","unstructured":"Chen Y, Pock T (2016) Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans Pattern Anal Mach Intell 39(6):1256\u20131272","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9314_CR42","doi-asserted-by":"crossref","unstructured":"Chen Y, Yu W and Pock T (2015) On learning optimized reaction diffusion processes for effective image restoration. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 5261\u20135269","DOI":"10.1109\/CVPR.2015.7299163"},{"key":"9314_CR43","doi-asserted-by":"crossref","unstructured":"Burger HC, Schuler CJ, Harmeling S (2012) Image denoising: Can plain neural networks compete with BM3D?. In: 2012 IEEE conference on computer vision and pattern recognition, IEEE, pp. 2392\u20132399","DOI":"10.1109\/CVPR.2012.6247952"},{"key":"9314_CR44","doi-asserted-by":"crossref","unstructured":"Chen J, Chen J, Chao H and Yang M (2018) Image blind denoising with generative adversarial network based noise modeling. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3155\u20133164","DOI":"10.1109\/CVPR.2018.00333"},{"key":"9314_CR45","unstructured":"Mao X, Shen C and Yang Y-B (2016) Image restoration using very deep convolutional encoder\u2013decoder networks with symmetric skip connections. In: Advances in neural information processing systems, pp. 2802\u20132810"},{"key":"9314_CR46","unstructured":"Xie J, Xu L and Chen E (2012) Image denoising and inpainting with deep neural networks. In: Advances in neural information processing systems, pp. 341\u2013349"},{"issue":"5","key":"9314_CR47","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/MSP.2017.2717489","volume":"34","author":"L Zhang","year":"2017","unstructured":"Zhang L, Zuo W (2017) Image restoration: From sparse and low-rank priors to deep priors [lecture notes]. IEEE Signal Process Mag 34(5):172\u2013179","journal-title":"IEEE Signal Process Mag"},{"issue":"10","key":"9314_CR48","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1109\/TPAMI.2018.2873610","volume":"41","author":"W Dong","year":"2018","unstructured":"Dong W, Wang P, Yin W, Shi G, Wu F, Lu X (2018) Denoising prior driven deep neural network for image restoration. IEEE Trans Pattern Anal Mach Intell 41(10):2305\u20132318","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"8","key":"9314_CR49","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1109\/LSP.2018.2850222","volume":"25","author":"C Cruz","year":"2018","unstructured":"Cruz C, Foi A, Katkovnik V, Egiazarian K (2018) Nonlocality-reinforced convolutional neural networks for image denoising. IEEE Signal Process Lett 25(8):1216\u20131220","journal-title":"IEEE Signal Process Lett"},{"issue":"6","key":"9314_CR50","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"key":"9314_CR51","unstructured":"Ioffe S and Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint: arXiv:1502.03167"},{"key":"9314_CR52","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S and Sun J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"9314_CR53","volume-title":"A wavelet tour of signal processing","author":"S Mallat","year":"1999","unstructured":"Mallat S (1999) A wavelet tour of signal processing. Elsevier"},{"key":"9314_CR54","doi-asserted-by":"crossref","unstructured":"Santhanam V, Morariu VI and Davis LS (2017) Generalized deep image to image regression. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5609\u20135619","DOI":"10.1109\/CVPR.2017.573"},{"key":"9314_CR55","doi-asserted-by":"crossref","unstructured":"Zhang K, Zuo W, Gu S and Zhang L (2017) Learning deep CNN denoiser prior for image restoration. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3929\u20133938","DOI":"10.1109\/CVPR.2017.300"},{"key":"9314_CR56","unstructured":"Mao X-J, Shen C and Yang Y-B (2016) Image restoration using convolutional auto-encoders with symmetric skip connections. arXiv preprint: arXiv:1606.08921"},{"key":"9314_CR57","doi-asserted-by":"crossref","unstructured":"Bae W, Yoo J and Chul Ye J (2017) Beyond deep residual learning for image restoration: Persistent homology-guided manifold simplification. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 145\u2013153","DOI":"10.1109\/CVPRW.2017.152"},{"key":"9314_CR58","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P and Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, Springer, pp. 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"9314_CR59","doi-asserted-by":"crossref","first-page":"116449","DOI":"10.1016\/j.image.2021.116449","volume":"99","author":"M Zhang","year":"2021","unstructured":"Zhang M, Yang C, Yuan Y, Guan Y, Wang S, Liu Q (2021) Multi-wavelet guided deep mean-shift prior for image restoration. Signal Process Image Commun 99:116449","journal-title":"Signal Process Image Commun"},{"key":"9314_CR60","doi-asserted-by":"crossref","first-page":"109050","DOI":"10.1016\/j.patcog.2022.109050","volume":"134","author":"C Tian","year":"2023","unstructured":"Tian C, Zheng M, Zuo W, Zhang B, Zhang Y, Zhang D (2023) Multi-stage image denoising with the wavelet transform. Pattern Recogn 134:109050","journal-title":"Pattern Recogn"},{"key":"9314_CR61","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X and Xu C (2017) Memnet: a persistent memory network for image restoration. In: Proceedings of the IEEE international conference on computer vision, pp. 4539\u20134547","DOI":"10.1109\/ICCV.2017.486"},{"key":"9314_CR62","first-page":"14099","volume":"35","author":"Y Gou","year":"2022","unstructured":"Gou Y, Hu P, Lv J, Zhou JT, Peng X (2022) Multi-scale adaptive network for single image denoising. Adv Neural Inf Process Syst 35:14099\u201314112","journal-title":"Adv Neural Inf Process Syst"},{"key":"9314_CR63","doi-asserted-by":"crossref","unstructured":"Ren C, He X, Wang C and Zhao Z (2021) Adaptive consistency prior based deep network for image denoising. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 8596\u20138606","DOI":"10.1109\/CVPR46437.2021.00849"},{"key":"9314_CR64","unstructured":"Zhang Y, Li K, Li K, Zhong B and Fu Y (2019) Residual non-local attention networks for image restoration. arXiv preprint: arXiv:1903.10082"},{"key":"9314_CR65","doi-asserted-by":"crossref","unstructured":"Jia X, Liu S, Feng X and Zhang L (2019) Focnet: a fractional optimal control network for image denoising. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 6054\u20136063.","DOI":"10.1109\/CVPR.2019.00621"},{"key":"9314_CR66","doi-asserted-by":"crossref","unstructured":"Xia Z and Chakrabarti A (2020) Identifying recurring patterns with deep neural networks for natural image denoising. In Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp. 2426\u20132434","DOI":"10.1109\/WACV45572.2020.9093586"},{"key":"9314_CR67","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.neucom.2018.12.075","volume":"345","author":"Y Peng","year":"2019","unstructured":"Peng Y, Zhang L, Liu S, Wu X, Zhang Y, Wang X (2019) Dilated residual networks with symmetric skip connection for image denoising. Neurocomputing 345:67\u201376","journal-title":"Neurocomputing"},{"key":"9314_CR68","doi-asserted-by":"crossref","first-page":"107639","DOI":"10.1016\/j.patcog.2020.107639","volume":"111","author":"Y Quan","year":"2021","unstructured":"Quan Y, Chen Y, Shao Y, Teng H, Xu Y, Ji H (2021) Image denoising using complex-valued deep CNN. Pattern Recogn 111:107639","journal-title":"Pattern Recogn"},{"key":"9314_CR69","doi-asserted-by":"crossref","first-page":"106949","DOI":"10.1016\/j.knosys.2021.106949","volume":"226","author":"C Tian","year":"2021","unstructured":"Tian C, Xu Y, Zuo W, Du B, Lin C-W, Zhang D (2021) Designing and training of a dual CNN for image denoising. Knowl-Based Syst 226:106949","journal-title":"Knowl-Based Syst"},{"key":"9314_CR70","doi-asserted-by":"crossref","unstructured":"Wang P et al. (2018) Understanding convolution for semantic segmentation. In: 2018 IEEE winter conference on applications of computer vision (WACV), IEEE, pp. 1451\u20131460","DOI":"10.1109\/WACV.2018.00163"},{"key":"9314_CR71","volume-title":"Multiresolution image processing and analysis","author":"A Rosenfeld","year":"2013","unstructured":"Rosenfeld A (2013) Multiresolution image processing and analysis. Springer"},{"key":"9314_CR72","doi-asserted-by":"crossref","unstructured":"Heaton J, Goodfellow I, Bengio Y and Courville A (2016) Deep learning. The MIT Press, 2016, 800 pp, ISBN: 0262035618. Genetic programming and evolvable machines, 19(1\u20132):pp. 305\u2013307, 2018","DOI":"10.1007\/s10710-017-9314-z"},{"issue":"11","key":"9314_CR73","doi-asserted-by":"crossref","first-page":"e21","DOI":"10.23915\/distill.00021","volume":"4","author":"A Araujo","year":"2019","unstructured":"Araujo A, Norris W, Sim J (2019) Computing receptive fields of convolutional neural networks. Distill 4(11):e21","journal-title":"Distill"},{"key":"9314_CR74","unstructured":"Bao H (2019) Investigations of the influences of a CNN's receptive field on segmentation of subnuclei of bilateral amygdalae. arXiv preprint: arXiv:1911.02761"},{"key":"9314_CR75","unstructured":"Yu F and Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv preprint: arXiv:1511.07122"},{"issue":"2","key":"9314_CR76","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TIP.2016.2631888","volume":"26","author":"K Ma","year":"2016","unstructured":"Ma K et al (2016) Waterloo exploration database: new challenges for image quality assessment models. IEEE Trans Image Process 26(2):1004\u20131016","journal-title":"IEEE Trans Image Process"},{"key":"9314_CR77","doi-asserted-by":"crossref","unstructured":"Agustsson E and Timofte R (2017) Ntire 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2017, pp. 126\u2013135","DOI":"10.1109\/CVPRW.2017.150"},{"key":"9314_CR78","unstructured":"Martin D, Fowlkes C, Tal D and Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings eighth IEEE international conference on computer vision. ICCV 2001, 2001, vol. 2:416\u2013423"},{"key":"9314_CR79","doi-asserted-by":"crossref","unstructured":"Huang J-B, Singh A and Ahuja N (2015) Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5197\u20135206","DOI":"10.1109\/CVPR.2015.7299156"},{"issue":"2","key":"9314_CR80","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1109\/JSTSP.2009.2015374","volume":"3","author":"AK Moorthy","year":"2009","unstructured":"Moorthy AK, Bovik AC (2009) Visual importance pooling for image quality assessment. IEEE J Sel Top Signal Process 3(2):193\u2013201","journal-title":"IEEE J Sel Top Signal Process"},{"key":"9314_CR81","unstructured":"Lei Ba J, Kiros JR and Hinton GE (2016) Layer normalization. ArXiv e-prints: arXiv:1607.06450"},{"issue":"2","key":"9314_CR82","doi-asserted-by":"crossref","first-page":"023016","DOI":"10.1117\/1.3600632","volume":"20","author":"L Zhang","year":"2011","unstructured":"Zhang L, Wu X, Buades A, Li X (2011) Color demosaicking by local directional interpolation and nonlocal adaptive thresholding. J Electron Imaging 20(2):023016","journal-title":"J Electron Imaging"},{"key":"9314_CR83","unstructured":"Kingma DP and Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint: arXiv:1412.6980"},{"key":"9314_CR84","doi-asserted-by":"crossref","unstructured":"Vedaldi A and Lenc K (2015) Matconvnet: convolutional neural networks for matlab. In: Proceedings of the 23rd ACM international conference on Multimedia, pp. 689\u2013692","DOI":"10.1145\/2733373.2807412"},{"issue":"3","key":"9314_CR85","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/97.995823","volume":"9","author":"Z Wang","year":"2002","unstructured":"Wang Z, Bovik AC (2002) A universal image quality index. IEEE Signal Process Lett 9(3):81\u201384","journal-title":"IEEE Signal Process Lett"},{"issue":"4","key":"9314_CR86","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"9314_CR87","unstructured":"Vaswani A et al. (2017) Attention is all you need. Advances in neural information processing systems, vol. 30"}],"updated-by":[{"DOI":"10.1007\/s00521-024-09528-x","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T00:00:00Z","timestamp":1707091200000}}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09314-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09314-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09314-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T20:13:00Z","timestamp":1709842380000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09314-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,8]]},"references-count":87,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["9314"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09314-1","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s00521-024-09528-x","asserted-by":"object"}]},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,8]]},"assertion":[{"value":"23 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2024","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00521-024-09528-x","URL":"https:\/\/doi.org\/10.1007\/s00521-024-09528-x","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Please note that there is no conflict of interests over this article, to the best of our knowledge.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Data generated during this study are available from the corresponding author on request.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data availability"}}]}}