{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T11:04:38Z","timestamp":1782644678220,"version":"3.54.5"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-17468-2","type":"journal-article","created":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T06:01:52Z","timestamp":1703052112000},"page":"58181-58199","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["A comprehensive review of image denoising in deep learning"],"prefix":"10.1007","volume":"83","author":[{"given":"Rusul Sabah","family":"Jebur","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohd Hazli Bin Mohamed","family":"Zabil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dalal Adulmohsin","family":"Hammood","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lim Kok","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,20]]},"reference":[{"key":"17468_CR1","doi-asserted-by":"crossref","unstructured":"Gupta A, Bhateja V, Srivastava A, Gupta A, Satapathy SC (2019) Speckle noise suppression in ultrasound images by using an improved non-local mean filter. In: Soft Computing and Signal Processing: Proceedings of ICSCSP 2018, Volume 2 (pp. 13\u201319). Springer Singapore","DOI":"10.1007\/978-981-13-3393-4_2"},{"issue":"3","key":"17468_CR2","first-page":"746","volume":"22","author":"A Awad","year":"2019","unstructured":"Awad A (2019) Denoising images corrupted with impulse, Gaussian, or a mixture of impulse and gaussian noise. Eng Sci Technol Int J 22(3):746\u2013753","journal-title":"Eng Sci Technol Int J"},{"key":"17468_CR3","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1007\/s11045-018-0616-y","volume":"30","author":"SK Randhawa","year":"2019","unstructured":"Randhawa SK, Sunkaria RK, Puthooran E (2019) Despeckling of ultrasound images using novel adaptive wavelet thresholding function. Multidimens Syst Signal Process 30:1545\u20131561","journal-title":"Multidimens Syst Signal Process"},{"key":"17468_CR4","doi-asserted-by":"crossref","first-page":"66898","DOI":"10.1109\/ACCESS.2020.2986827","volume":"8","author":"YH Shin","year":"2020","unstructured":"Shin YH, Park MJ, Lee OY, Kim JO (2020) Deep orthogonal transform feature for image denoising. IEEE Access 8:66898\u201366909","journal-title":"IEEE Access"},{"key":"17468_CR5","doi-asserted-by":"crossref","first-page":"4885","DOI":"10.1109\/TIP.2020.2976814","volume":"29","author":"M El Helou","year":"2020","unstructured":"El Helou M, S\u00fcsstrunk S (2020) Blind universal bayesian image denoising with gaussian noise level learning. IEEE Trans Image Process 29:4885\u20134897","journal-title":"IEEE Trans Image Process"},{"key":"17468_CR6","doi-asserted-by":"crossref","first-page":"52378","DOI":"10.1109\/ACCESS.2021.3069236","volume":"9","author":"H Sun","year":"2021","unstructured":"Sun H, Peng L, Zhang H, He Y, Cao S, Lu L (2021) Dynamic PET image denoising using deep image prior combined with regularization by denoising. IEEE Access 9:52378\u201352392","journal-title":"IEEE Access"},{"key":"17468_CR7","doi-asserted-by":"crossref","first-page":"22420","DOI":"10.1109\/ACCESS.2022.3152219","volume":"10","author":"T Zin","year":"2022","unstructured":"Zin T, Seta S, Nakahara Y, Yamaguchi T, Ikehara M (2022) Local image denoising using RAISR. IEEE Access 10:22420\u201322428","journal-title":"IEEE Access"},{"key":"17468_CR8","doi-asserted-by":"crossref","first-page":"11923","DOI":"10.1109\/ACCESS.2023.3242050","volume":"11","author":"Z Li","year":"2023","unstructured":"Li Z, Liu H, Cheng L, Jia X (2023) Image denoising algorithm based on gradient domain guided filtering and NSST. IEEE Access 11:11923\u201311933","journal-title":"IEEE Access"},{"key":"17468_CR9","doi-asserted-by":"crossref","first-page":"14340","DOI":"10.1109\/ACCESS.2023.3243829","volume":"11","author":"D Zhang","year":"2023","unstructured":"Zhang D, Zhou F (2023) Self-supervised image denoising for real-world images with context-aware transformer. IEEE Access 11:14340\u201314349","journal-title":"IEEE Access"},{"key":"17468_CR10","doi-asserted-by":"crossref","first-page":"110414","DOI":"10.1109\/ACCESS.2019.2934178","volume":"7","author":"HS Park","year":"2019","unstructured":"Park HS, Baek J, You SK, Choi JK, Seo JK (2019) Unpaired image denoising using a generative adversarial network in X-ray CT. IEEE Access 7:110414\u2013110425","journal-title":"IEEE Access"},{"key":"17468_CR11","doi-asserted-by":"crossref","first-page":"2780","DOI":"10.1007\/s00259-019-04468-4","volume":"46","author":"J Cui","year":"2019","unstructured":"Cui J, Gong K, Guo N, Wu C, Meng X, Kim K, Zheng K, Wu Z, Fu L, Xu B, Zhu Z (2019) PET image denoising using unsupervised deep learning. Eur J Nucl Med Mol Imaging 46:2780\u20132789","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"17468_CR12","doi-asserted-by":"crossref","first-page":"127622","DOI":"10.1109\/ACCESS.2020.3008324","volume":"8","author":"X Wang","year":"2020","unstructured":"Wang X, Li Z, Shan H, Tian Z, Ren Y, Zhou W (2020) Fastderainnet: a deep learning algorithm for single image deraining. IEEE Access 8:127622\u2013127630","journal-title":"IEEE Access"},{"key":"17468_CR13","doi-asserted-by":"crossref","first-page":"62266","DOI":"10.1109\/ACCESS.2021.3073944","volume":"9","author":"M Tian","year":"2021","unstructured":"Tian M, Song K (2021) Boosting magnetic resonance image denoising with generative adversarial networks. IEEE Access 9:62266\u201362275","journal-title":"IEEE Access"},{"issue":"10","key":"17468_CR14","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1109\/TMI.2022.3168793","volume":"41","author":"YA Bayhaqi","year":"2022","unstructured":"Bayhaqi YA, Hamidi A, Canbaz F, Navarini AA, Cattin PC, Zam A (2022) Deep-learning-based fast Optical Coherence Tomography (OCT) image denoising for smart laser osteotomy. IEEE Trans Med Imaging 41(10):2615\u20132628","journal-title":"IEEE Trans Med Imaging"},{"key":"17468_CR15","doi-asserted-by":"crossref","first-page":"26667","DOI":"10.1109\/ACCESS.2023.3255641","volume":"11","author":"W Sereethavekul","year":"2023","unstructured":"Sereethavekul W, Ekpanyapong M (2023) Adaptive lightweight license plate image recovery using deep learning based on generative adversarial network. IEEE Access 11:26667\u201326685","journal-title":"IEEE Access"},{"key":"17468_CR16","doi-asserted-by":"crossref","first-page":"96594","DOI":"10.1109\/ACCESS.2019.2929230","volume":"7","author":"F Hashimoto","year":"2019","unstructured":"Hashimoto F, Ohba H, Ote K, Teramoto A, Tsukada H (2019) Dynamic PET image denoising using deep convolutional neural networks without prior training datasets. IEEE Access 7:96594\u201396603","journal-title":"IEEE Access"},{"key":"17468_CR17","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.1109\/TIP.2019.2940496","volume":"29","author":"F Wang","year":"2019","unstructured":"Wang F, Huang H, Liu J (2019) Variational-based mixed noise removal with CNN deep learning regularization. IEEE Trans Image Process 29:1246\u20131258","journal-title":"IEEE Trans Image Process"},{"key":"17468_CR18","doi-asserted-by":"crossref","first-page":"104728","DOI":"10.1109\/ACCESS.2020.2999965","volume":"8","author":"X Zhang","year":"2020","unstructured":"Zhang X, Gao P, Zhao K, Liu S, Li G, Yin L (2020) Image restoration via deep memory-based latent attention network. IEEE Access 8:104728\u2013104739","journal-title":"IEEE Access"},{"key":"17468_CR19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11760-019-01537-x","volume":"15","author":"R Lan","year":"2021","unstructured":"Lan R, Zou H, Pang C, Zhong Y, Liu Z, Luo X (2021) Image denoising via deep residual convolutional neural networks. SIViP 15:1\u20138","journal-title":"SIViP"},{"key":"17468_CR20","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102859","volume":"69","author":"S Rawat","year":"2021","unstructured":"Rawat S, Rana KPS, Kumar V (2021) A novel complex-valued convolutional neural network for medical image denoising. Biomed Signal Process Control 69:102859","journal-title":"Biomed Signal Process Control"},{"key":"17468_CR21","doi-asserted-by":"crossref","first-page":"31742","DOI":"10.1109\/ACCESS.2021.3061062","volume":"9","author":"J Gurrola-Ramos","year":"2021","unstructured":"Gurrola-Ramos J, Dalmau O, Alarc\u00f3n TE (2021) A residual dense u-net neural network for image denoising. IEEE Access 9:31742\u201331754","journal-title":"IEEE Access"},{"key":"17468_CR22","doi-asserted-by":"crossref","first-page":"49657","DOI":"10.1109\/ACCESS.2022.3169131","volume":"10","author":"Y Meng","year":"2022","unstructured":"Meng Y, Zhang J (2022) A novel gray image denoising method using convolutional neural network. IEEE Access 10:49657\u201349676","journal-title":"IEEE Access"},{"key":"17468_CR23","doi-asserted-by":"crossref","unstructured":"Zhang Q, Xiao J, Tian C, Chun\u2010Wei Lin J, Zhang S (2023) A robust deformed convolutional neural network (CNN) for image denoising. CAAI Transactions on Intelligence Technology 8(2):331\u2013342","DOI":"10.1049\/cit2.12110"},{"key":"17468_CR24","doi-asserted-by":"crossref","first-page":"122286","DOI":"10.1109\/ACCESS.2022.3222826","volume":"10","author":"SE Lee","year":"2022","unstructured":"Lee SE, Woo SM, Kim JH, Ryu JH, Kim JO (2022) Deep region adaptive denoising for texture enhancement. IEEE Access 10:122286\u2013122301","journal-title":"IEEE Access"},{"key":"17468_CR25","doi-asserted-by":"crossref","first-page":"9613","DOI":"10.1109\/ACCESS.2023.3254893","volume":"11","author":"S Holla","year":"2023","unstructured":"Holla S, Park N, Lee B (2023) EFID: edge-focused image denoising using a convolutional neural network. IEEE Access 11:9613\u20139626","journal-title":"IEEE Access"},{"key":"17468_CR26","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.chaos.2018.01.022","volume":"108","author":"P Shi","year":"2018","unstructured":"Shi P, Xia H, Han D, Fu R, Yuan D (2018) Stochastic resonance in a time polo-delayed asymmetry bistable system driven by multiplicative white noise and additive color noise. Chaos Solitons Fractals 108:8\u201314","journal-title":"Chaos Solitons Fractals"},{"key":"17468_CR27","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1007\/s00371-017-1362-0","volume":"34","author":"A Khmag","year":"2018","unstructured":"Khmag A, Ramli AR, Al-Haddad SAR, Kamarudin N (2018) Natural image noise level estimation based on local statistics for blind noise reduction. Visual Comput 34:575\u2013587","journal-title":"Visual Comput"},{"key":"17468_CR28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.probengmech.2018.03.002","volume":"53","author":"J Chen","year":"2018","unstructured":"Chen J, Rui Z (2018) Dimension-reduced FPK equation for additive white-noise excited nonlinear structures. Probab Eng Mech 53:1\u201313","journal-title":"Probab Eng Mech"},{"issue":"9","key":"17468_CR29","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1109\/LSP.2019.2929863","volume":"26","author":"A Dytso","year":"2019","unstructured":"Dytso A, Cardone M, Poor HV (2019) On estimating the norm of a gaussian vector under additive white gaussian noise. IEEE Signal Process Lett 26(9):1325\u20131329","journal-title":"IEEE Signal Process Lett"},{"key":"17468_CR30","doi-asserted-by":"crossref","DOI":"10.1016\/j.automatica.2020.108879","volume":"115","author":"U Soverini","year":"2020","unstructured":"Soverini U, S\u00f6derstr\u00f6m T (2020) Frequency domain identification of FIR models in the presence of additive input\u2013output noise. Automatica 115:108879","journal-title":"Automatica"},{"key":"17468_CR31","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2020.109840","volume":"137","author":"MA Akinlar","year":"2020","unstructured":"Akinlar MA, Inc M, G\u00f3mez-Aguilar JF, Boutarfa B (2020) Solutions of a disease model with fractional white noise. Chaos Solitons Fractals 137:109840","journal-title":"Chaos Solitons Fractals"},{"key":"17468_CR32","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1109\/ACCESS.2016.2549546","volume":"4","author":"Q Zhu","year":"2016","unstructured":"Zhu Q, Wu N, Qiao Y, Zhou M (2016) Optimal scheduling of complex multi-cluster tools based on timed resource-oriented Petri nets. IEEE Access 4:2096\u20132109","journal-title":"IEEE Access"},{"issue":"6","key":"17468_CR33","doi-asserted-by":"crossref","first-page":"2996","DOI":"10.1109\/TIP.2018.2811546","volume":"27","author":"J Xu","year":"2018","unstructured":"Xu J, Zhang L, Zhang D (2018) External prior guided internal prior learning for real-world noisy image denoising. IEEE Trans Image Process 27(6):2996\u20133010","journal-title":"IEEE Trans Image Process"},{"issue":"9","key":"17468_CR34","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":"17468_CR35","doi-asserted-by":"crossref","unstructured":"Zhao Y, Jiang Z, Men A, Ju G (2019) Pyramid real image denoising network. In: 2019 IEEE Visual Communications and Image Processing (VCIP). IEEE, pp 1\u20134","DOI":"10.1109\/VCIP47243.2019.8965754"},{"issue":"12","key":"17468_CR36","doi-asserted-by":"crossref","first-page":"3071","DOI":"10.1109\/TPAMI.2019.2921548","volume":"42","author":"C Chen","year":"2019","unstructured":"Chen C, Xiong Z, Tian X, Zha ZJ, Wu F (2019) Real-world image denoising with deep boosting. IEEE Trans Pattern Anal Mach Intell 42(12):3071\u20133087","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"17468_CR37","doi-asserted-by":"crossref","first-page":"2124","DOI":"10.1109\/LSP.2020.3039726","volume":"27","author":"Y Song","year":"2020","unstructured":"Song Y, Zhu Y, Du X (2020) Grouped multi-scale network for real-world image denoising. IEEE Signal Process Lett 27:2124\u20132128","journal-title":"IEEE Signal Process Lett"},{"key":"17468_CR38","doi-asserted-by":"crossref","first-page":"124647","DOI":"10.1109\/ACCESS.2019.2938799","volume":"7","author":"J Chen","year":"2019","unstructured":"Chen J, Zhang G, Xu S, Yu H (2019) A blind CNN denoising model for random-valued impulse noise. IEEE Access 7:124647\u2013124661","journal-title":"IEEE Access"},{"issue":"1","key":"17468_CR39","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/LCOMM.2019.2952845","volume":"24","author":"Y Jin","year":"2019","unstructured":"Jin Y, Zhang J, Ai B, Zhang X (2019) Channel estimation for mmWave massive MIMO with convolutional blind denoising network. IEEE Commun Lett 24(1):95\u201398","journal-title":"IEEE Commun Lett"},{"key":"17468_CR40","doi-asserted-by":"crossref","unstructured":"Zhu S, Xu G, Cheng Y, Han X, Wang Z (2019) BDGAN: Image blind denoising using generative adversarial networks. In: Pattern Recognition and Computer Vision: Second Chinese Conference, PRCV 2019, Xi\u2019an, China, November 8\u201311, 2019, Proceedings, Part II 2 (pp. 241\u2013252). Springer International Publishing","DOI":"10.1007\/978-3-030-31723-2_21"},{"key":"17468_CR41","doi-asserted-by":"crossref","unstructured":"Goncharova AS, Honigmann A, Jug F, Krull A (2020) Improving blind spot denoising for microscopy. In: Computer Vision\u2013ECCV 2020 Workshops: Glasgow, UK, August 23\u201328, 2020, Proceedings, Part I 16 (pp. 380\u2013393). Springer International Publishing","DOI":"10.1007\/978-3-030-66415-2_25"},{"key":"17468_CR42","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.ins.2021.04.045","volume":"570","author":"DM Vo","year":"2021","unstructured":"Vo DM, Nguyen DM, Le TP, Lee SW (2021) HI-GAN: a hierarchical generative adversarial network for blind denoising of real photographs. Inf Sci 570:225\u2013240","journal-title":"Inf Sci"},{"key":"17468_CR43","first-page":"1","volume":"60","author":"Y Yuan","year":"2021","unstructured":"Yuan Y, Ma H, Liu G (2021) Partial-DNet: a novel blind denoising model with noise intensity estimation for HSI. IEEE Trans Geosci Remote Sens 60:1\u201313","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"17468_CR44","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.ijleo.2017.11.116","volume":"157","author":"S Routray","year":"2018","unstructured":"Routray S, Ray AK, Mishra C, Palai G (2018) Efficient hybrid image denoising scheme based on SVM classification. Optik 157:503\u2013511","journal-title":"Optik"},{"issue":"2","key":"17468_CR45","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s00477-017-1400-5","volume":"32","author":"V Nourani","year":"2018","unstructured":"Nourani V, Partoviyan A (2018) Hybrid denoising-jittering data pre-processing approach to enhance multi-step-ahead rainfall\u2013runoff modeling. Stochastic Environ Res Risk Assess 32(2):545\u2013562","journal-title":"Stochastic Environ Res Risk Assess"},{"key":"17468_CR46","doi-asserted-by":"crossref","unstructured":"Das K, Maitra M, Sharma P, Banerjee M (2019) Early started hybrid denoising technique for medical images. Recent trends in Signal and Image Processing: ISSIP 2017. Springer Singapore, pp 131\u2013140","DOI":"10.1007\/978-981-10-8863-6_14"},{"key":"17468_CR47","doi-asserted-by":"crossref","first-page":"57451","DOI":"10.1109\/ACCESS.2020.2982535","volume":"8","author":"A Abubakar","year":"2020","unstructured":"Abubakar A, Zhao X, Takruri M, Bastaki E, Bermak A (2020) A hybrid denoising algorithm of BM3D and KSVD for gaussian noise in DoFP polarization images. IEEE Access 8:57451\u201357459","journal-title":"IEEE Access"},{"key":"17468_CR48","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1007\/s40995-020-00977-2","volume":"44","author":"F Kazemi Golbaghi","year":"2020","unstructured":"Kazemi Golbaghi F, Rezghi M, Eslahchi MR (2020) A hybrid image denoising method based on integer and fractional-order total variation. Iran J Sci Technol Trans A: Sci 44:1803\u20131814","journal-title":"Iran J Sci Technol Trans A: Sci"},{"key":"17468_CR49","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102337","volume":"65","author":"C Kaur","year":"2021","unstructured":"Kaur C, Bisht A, Singh P, Joshi G (2021) EEG Signal denoising using hybrid approach of Variational Mode Decomposition and wavelets for depression. Biomed Signal Process Control 65:102337","journal-title":"Biomed Signal Process Control"},{"key":"17468_CR50","doi-asserted-by":"crossref","unstructured":"Manj\u00f3n JV, Coupe P (2018) MRI denoising using deep learning. In: Patch-Based Techniques in Medical Imaging: 4th International Workshop, Patch-MI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4 (pp. 12\u201319). Springer International Publishing","DOI":"10.1007\/978-3-030-00500-9_2"},{"key":"17468_CR51","doi-asserted-by":"crossref","unstructured":"Gondara L, Wang K (2018) Mida: Multiple imputation using denoising autoencoders. In: Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3\u20136, 2018, Proceedings, Part III 22 (pp. 260\u2013272). Springer International Publishing","DOI":"10.1007\/978-3-319-93040-4_21"},{"key":"17468_CR52","doi-asserted-by":"crossref","unstructured":"Tassano M, Delon J, Veit T (2019) Dvdnet: A fast network for deep video denoising. In: 2019 IEEE International Conference on Image Processing (ICIP) (pp. 1805\u20131809). IEEE","DOI":"10.1109\/ICIP.2019.8803136"},{"key":"17468_CR53","doi-asserted-by":"crossref","unstructured":"Davy A, Ehret T, Morel JM, Arias P, Facciolo G (2019) A non-local CNN for video denoising. In: 2019 IEEE International Conference on Image Processing (ICIP). IEEE, pp 2409\u20132413","DOI":"10.1109\/ICIP.2019.8803314"},{"key":"17468_CR54","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.media.2019.03.004","volume":"54","author":"P Liu","year":"2019","unstructured":"Liu P, Basha E, Li MD, Xiao Y, Sanelli Y, Fang R (2019) Deep evolutionary networks with expedited genetic algorithms for medical image denoising. Med Image Anal 54:306\u2013315","journal-title":"Med Image Anal"},{"key":"17468_CR55","doi-asserted-by":"crossref","unstructured":"Thanh DN, Prasath VS, Erkan U (2019). An improved BPDF filter for high density salt and pepper denoising. In: 2019 IEEE-RIVF International Conference on Computing and Communication Technologies (RIVF) (pp. 1\u20135). IEEE","DOI":"10.1109\/RIVF.2019.8713669"},{"key":"17468_CR56","doi-asserted-by":"crossref","first-page":"12043","DOI":"10.1007\/s11042-018-6732-8","volume":"78","author":"B Fu","year":"2019","unstructured":"Fu B, Zhao X, Song C, Li X, Wang X (2019) A salt and pepper noise image denoising method based on the generative classification. Multimed Tools Appl 78:12043\u201312053","journal-title":"Multimed Tools Appl"},{"issue":"29\u201330","key":"17468_CR57","doi-asserted-by":"crossref","first-page":"21013","DOI":"10.1007\/s11042-020-08887-6","volume":"79","author":"DN Thanh","year":"2020","unstructured":"Thanh DN, Hai NH, Prasath VS, Hieu LM, Tavares JMR (2020) A two-stage filter for high density salt and pepper denoising. Multimed Tools Appl 79(29\u201330):21013\u201321035","journal-title":"Multimed Tools Appl"},{"key":"17468_CR58","doi-asserted-by":"crossref","first-page":"163677","DOI":"10.1016\/j.ijleo.2019.163677","volume":"208","author":"DNH Thanh","year":"2020","unstructured":"Thanh DNH, Hien NN, Prasath S (2020) Adaptive total variation L1 regularization for salt and pepper image denoising. Optik 208:163677","journal-title":"Optik"},{"key":"17468_CR59","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.neucom.2021.02.010","volume":"442","author":"L Liang","year":"2021","unstructured":"Liang L, Deng S, Gueguen L, Wei M, Wu X, Qin J (2021) Convolutional neural network with median layers for denoising salt-and-pepper contaminations. Neurocomputing 442:26\u201335","journal-title":"Neurocomputing"},{"key":"17468_CR60","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.ijleo.2018.08.013","volume":"173","author":"G Wang","year":"2018","unstructured":"Wang G, Liu Y, Xiong W, Li Y (2018) An improved non-local means filter for color image denoising. Optik 173:157\u2013173","journal-title":"Optik"},{"key":"17468_CR61","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jcp.2018.02.009","volume":"362","author":"P Tsoutsanis","year":"2018","unstructured":"Tsoutsanis P (2018) Extended bounds limiter for high-order finite-volume schemes on unstructured meshes. J Comput Phys 362:69\u201394","journal-title":"J Comput Phys"},{"key":"17468_CR62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-018-1148-6","volume":"43","author":"E Punarselvam","year":"2019","unstructured":"Punarselvam E, Suresh P (2019) Non-linear filtering technique used for testing the human lumbar spine FEA model. J Med Syst 43:1\u201313","journal-title":"J Med Syst"},{"key":"17468_CR63","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.procs.2020.04.029","volume":"171","author":"BR Manju","year":"2020","unstructured":"Manju BR, Sneha MR (2020) ECG denoising using Wiener filter and kalman filter. Procedia Comput Sci 171:273\u2013281","journal-title":"Procedia Comput Sci"},{"key":"17468_CR64","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijleo.2021.167564","volume":"244","author":"M Kaur","year":"2021","unstructured":"Kaur M, Sarkar RK, Dutta MK (2021) Investigation on quality enhancement of old and fragile artworks using non-linear filter and histogram equalization techniques. Optik 244:167564","journal-title":"Optik"},{"issue":"2","key":"17468_CR65","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1007\/s11760-022-02262-8","volume":"17","author":"B Fu","year":"2023","unstructured":"Fu B, Dong Y, Fu S, Wu Y, Ren Y, Thanh DN (2023) Multistage supervised contrastive learning for hybrid-degraded image restoration. SIViP 17(2):573\u2013581","journal-title":"SIViP"},{"key":"17468_CR66","volume":"45","author":"B Fu","year":"2022","unstructured":"Fu B, Zhang X, Wang L, Ren Y, Thanh DN (2022) Double enhanced residual network for biological image denoising. Gene Expr Patterns 45:119270","journal-title":"Gene Expr Patterns"},{"issue":"3","key":"17468_CR67","first-page":"531","volume":"30","author":"B Fu","year":"2022","unstructured":"Fu B, Zhang X, Wang L, Ren Y, Thanh DN (2022) A blind medical image denoising method with noise generation network. J X-Ray Sci Technol 30(3):531\u2013547","journal-title":"J X-Ray Sci Technol"},{"issue":"1","key":"17468_CR68","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1002\/ima.22658","volume":"32","author":"B Fu","year":"2022","unstructured":"Fu B, Dong Y, Fu S, Mao Y, Thanh DN (2022) Learning domain transfer for unsupervised magnetic resonance imaging restoration and edge enhancement. Int J Imaging Syst Technol 32(1):144\u2013154","journal-title":"Int J Imaging Syst Technol"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17468-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17468-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17468-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T09:19:03Z","timestamp":1717406343000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17468-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,20]]},"references-count":68,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["17468"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17468-2","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,20]]},"assertion":[{"value":"24 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data sharing"}},{"value":"The authors declare that we have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}