{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T22:53:25Z","timestamp":1781650405959,"version":"3.54.5"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T00:00:00Z","timestamp":1779840000000},"content-version":"vor","delay-in-days":42,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Manipal University Jaipur"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Noise removal means eliminating noise from a noisy image, thereby improving the quality of original image. Elimination of noise from the input signal remains a major issue for investigators. With the increasing number of digital images captured day-to-day, the requirement for more accurate perceptibly appealing image is enhancing. However, images taken by contemporary cameras are automatically deteriorated by noise, resulting in degraded visual quality. In general, retrieval of essential data from noisy images in the procedure of denoising to acquire the best quality of images is a big issue today. Therefore, work must be done to eliminate noise in the image without falling image characteristics, like edges, corners, and other sharp frameworks. Hence, this research paper overviews numerous techniques for image denoising and image quality enhancement. This overview investigates 50 Research papers related to noise removal and image quality enhancement, and developed technique-wise reviews, namely spatial domain-based techniques, optimization-based approaches, transform domain-based approaches deep learning (DL)-based techniques and machine learning (ML)-based techniques. An investigation participate in a survey based upon classifying experiment approaches, datasets, year of publication, toolset employed, effectual metrics for image denoising and image quality improvement. Finally, the challenges of overviewed techniques are illustrated to concentrate investigators for developing various efficient techniques for image denoising and image quality improvement.<\/jats:p>","DOI":"10.1007\/s44196-026-01282-3","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T08:34:25Z","timestamp":1776242065000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Empirical Study for Image Denoising and Image Quality Enhancement: A Challenging Overview"],"prefix":"10.1007","volume":"19","author":[{"given":"Ashish","family":"Saini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasib Singh","family":"Gill","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Preeti","family":"Gulia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1136-6123","authenticated-orcid":false,"given":"Arshad","family":"Hashmi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piyush Kumar","family":"Shukla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susheela","family":"Vishnoi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"1282_CR1","doi-asserted-by":"crossref","unstructured":"Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising with block-matching and 3D filtering. In: Image processing: algorithms and systems, neural networks, and machine learning, SPIE. 6064, 354\u2013365 (2006)","DOI":"10.1117\/12.643267"},{"key":"1282_CR2","doi-asserted-by":"crossref","unstructured":"Pires, R.G., Santos, D.F.S., Pereira, L.A.M., De Souza, G.B., Levada, A.L.M., Papa, J.P.: A robust restricted boltzmann machine for binary image denoising, In 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI),IEEE. pp.390\u2013396 (2017)","DOI":"10.1109\/SIBGRAPI.2017.58"},{"key":"1282_CR3","doi-asserted-by":"crossref","unstructured":"Liu, Z., Yan, W.Q., Yang, M.L.: Image denoising based on a CNN model. In: 4th International conference on control, automation and robotics (ICCAR), IEEE. pp.389\u2013393 (2018)","DOI":"10.1109\/ICCAR.2018.8384706"},{"issue":"1","key":"1282_CR4","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1109\/MSP.2011.2179329","volume":"30","author":"P Milanfar","year":"2012","unstructured":"Milanfar, P.: A tour of modern image filtering: New insights and methods, both practical and theoretical. IEEE. Signal. Process. Mag. 30(1), 106\u2013128 (2012)","journal-title":"IEEE. Signal. Process. Mag."},{"key":"1282_CR5","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.eswa.2017.06.033","volume":"88","author":"T Veerakumar","year":"2017","unstructured":"Veerakumar, T., Subudhi, B.N., Esakkirajan, S., Pradhan, P.K.: Context model-based edge preservation filter for impulse noise removal. Expert Syst. Appl. 88, 29\u201344 (2017)","journal-title":"Expert Syst. Appl."},{"key":"1282_CR6","doi-asserted-by":"publisher","first-page":"112985","DOI":"10.1109\/ACCESS.2020.3003874","volume":"8","author":"A Singh","year":"2020","unstructured":"Singh, A., Sethi, G., Kalra, G.S.: Spatially adaptive image denoising via enhanced noise detection method for grayscale and color images. IEEE Access. 8, 112985\u2013113002 (2020)","journal-title":"IEEE Access."},{"key":"1282_CR7","doi-asserted-by":"crossref","unstructured":"Ieng, S.S., Tarel, J.P., Charbonnier, P.: Modeling non-gaussian noise for robust image analysis, In VISAPP. (1),183\u2013190 (2007)","DOI":"10.5220\/0002040901830190"},{"key":"1282_CR8","doi-asserted-by":"crossref","unstructured":"Hanji, G., Latte, M.V.: A new impulse noise detection and filtering algorithm. Image Processing & Communications (IPC), The Journal of University of Technology and Life Sciences in Bydgoszcz. 16(1\u20132), 43\u201348 (2012)","DOI":"10.2478\/v10248-012-0004-4"},{"issue":"11","key":"1282_CR9","first-page":"170","volume":"9","author":"V Jayaraj","year":"2009","unstructured":"Jayaraj, V., Ebenezer, D., Aiswarya, K.: High density salt and pepper noise removal in images using improved adaptive statistics estimation filter. Int. J. Comput. Sci. Netw. Secur. 9(11), 170\u2013175 (2009)","journal-title":"Int. J. Comput. Sci. Netw. Secur."},{"issue":"2","key":"1282_CR10","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1109\/TPAMI.2007.1176","volume":"30","author":"C Liu","year":"2007","unstructured":"Liu, C., Szeliski, R., Kang, S.B., Zitnick, C.L., Freeman, W.T.: Automatic estimation and removal of noise from a single image. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 299\u2013314 (2007)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"1282_CR11","doi-asserted-by":"publisher","first-page":"32","DOI":"10.48550\/arXiv.1002.1148","volume":"7","author":"SS Al-Amri","year":"2010","unstructured":"Al-Amri, S.S., Kalyankar, N.V., Khamitkar, S.D.: A comparative study of removal noise from remote sensing image. International Journal of Computer Science Issues (IJCSI). 7(1), 32-36 (2010) https:\/\/doi.org\/10.48550\/arXiv.1002.1148","journal-title":"International Journal of Computer Science Issues (IJCSI)."},{"issue":"2","key":"1282_CR12","first-page":"1","volume":"2","author":"JS Marcel","year":"2012","unstructured":"Marcel, J.S., Jayachandran, A., Sundararaj, G.K.: An efficient algorithm for removal of impulse noise using Adaptive Fuzzy Switching Weighted Median Filter. Int. J. Comput. Technol. Electron. Eng. (IJCTEE). 2(2), 1-8 (2012)","journal-title":"Int. J. Comput. Technol. Electron. Eng. (IJCTEE)."},{"issue":"6","key":"1282_CR13","doi-asserted-by":"publisher","first-page":"424","DOI":"10.48550\/arXiv.1302.1007","volume":"2","author":"FA Jassim","year":"2013","unstructured":"Jassim, F.A.: Image denoising using interquartile range filter with local averaging. International Journal of Soft Computing and Engineering (IJSCE). 2(6), 424-428 (2013) https:\/\/doi.org\/10.48550\/arXiv.1302.1007","journal-title":"International Journal of Soft Computing and Engineering (IJSCE)."},{"key":"1282_CR14","doi-asserted-by":"crossref","unstructured":"Judith, G., Kumarasabapathy, N.: Study and analysis of impulse noise reduction filters. Signal & Image Processing: An International Journal (SIPIJ). 2(1), 82\u201392 (2011)","DOI":"10.5121\/sipij.2011.2107"},{"issue":"4","key":"1282_CR15","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/S0305-9006(03)00066-7","volume":"61","author":"J Rogan","year":"2004","unstructured":"Rogan, J., Chen, D.: Remote sensing technology for mapping and monitoring land-cover and land-use change. Progress Plann. 61(4), 301\u2013325 (2004)","journal-title":"Progress Plann."},{"key":"1282_CR16","doi-asserted-by":"crossref","unstructured":"Yuan, J., Zheng, Y., Xie, X.: Discovering regions of different functions in a city using human mobility and POIs. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. pp.186\u2013194, (2012)","DOI":"10.1145\/2339530.2339561"},{"key":"1282_CR17","doi-asserted-by":"crossref","unstructured":"Boudriki Semlali, B.E., Freitag, F.: Sat-hadoop-processor: a distributed remote sensing big data processing software for earth observation applications, Applied Sciences. 11(22), 10610 (2021)","DOI":"10.3390\/app112210610"},{"key":"1282_CR18","doi-asserted-by":"crossref","unstructured":"Bhowmik, P., Pantho, M.J.H., Bobda, C., Harp: Hierarchical attention-oriented region-based processing for high-performance computation in vision sensor, Sensors. 21(5), 1757 (2021)","DOI":"10.3390\/s21051757"},{"key":"1282_CR19","doi-asserted-by":"crossref","unstructured":"Hung, S.C., Wu, H.C., Tseng, M.H.: Integrating Image Quality Enhancement Methods and Deep Learning Techniques for Remote Sensing Scene Classification, Applied Sciences. 11(24) pp.11659 (2021)","DOI":"10.3390\/app112411659"},{"issue":"12","key":"1282_CR20","doi-asserted-by":"publisher","first-page":"10348","DOI":"10.1109\/TGRS.2020.3045273","volume":"59","author":"Q Shi","year":"2021","unstructured":"Shi, Q., Tang, X., Yang, T., Liu, R., Zhang, L.: Hyperspectral image denoising using a 3-D attention denoising network. IEEE Trans. Geosci. Remote Sens. 59(12), 10348\u201310363 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"1282_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wu, R.: Fast depth image denoising and enhancement using a deep convolutional network. In: IEEE international conference on acoustics, speech and signal processing (ICASSP), pp.2499\u20132503 (2016)","DOI":"10.1109\/ICASSP.2016.7472127"},{"key":"1282_CR22","unstructured":"Yang, Q., Yan, P., Kalra, M.K., Wang, G.: CT image denoising with perceptive deep neural networks. arXiv preprint arXiv: 1702.07019 (2017)"},{"issue":"1","key":"1282_CR23","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s11604-018-0796-2","volume":"37","author":"T Higaki","year":"2019","unstructured":"Higaki, T., Nakamura, Y., Tatsugami, F., Nakaura, T., Awai, K.: Improvement of image quality at CT and MRI using deep learning. Japanese J. Radiol. 37(1), 73\u201380 (2019)","journal-title":"Japanese J. Radiol."},{"issue":"1","key":"1282_CR24","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1049\/trit.2018.1054","volume":"4","author":"C Tian","year":"2019","unstructured":"Tian, C., Xu, Y., Fei, L., Wang, J., Wen, J., Luo, N.: Enhanced CNN for image denoising. CAAI Trans. Intell. Technol. 4(1), 17\u201323 (2019)","journal-title":"CAAI Trans. Intell. Technol."},{"key":"1282_CR25","doi-asserted-by":"crossref","unstructured":"Anwar, S., Barnes, N.: Real image denoising with feature attention,. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 3155\u20133164 (2019)","DOI":"10.1109\/ICCV.2019.00325"},{"issue":"4","key":"1282_CR26","doi-asserted-by":"publisher","first-page":"2201","DOI":"10.1007\/s42835-021-00728-2","volume":"16","author":"C Zhang","year":"2021","unstructured":"Zhang, C., Sun, X.W., Xu, J., Huang, X.Y., Yu, G.Y., Park, S.H.: A Generative Adversarial Network to Denoise Depth Maps for Quality Improvement of DIBR-Synthesized Stereoscopic Images. J. Electr. Eng. Technol. 16(4), 2201\u20132210 (2021)","journal-title":"J. Electr. Eng. Technol."},{"key":"1282_CR27","doi-asserted-by":"crossref","unstructured":"Liao, X., Zhang, X.: Multi-scale mutual feature convolutional neural network for depth image denoise and enhancement. In: IEEE Visual Communications and Image Processing (VCIP). pp.1\u20134 (2017)","DOI":"10.1109\/VCIP.2017.8305145"},{"issue":"6","key":"1282_CR28","first-page":"857","volume":"25","author":"Y Sun","year":"2017","unstructured":"Sun, Y., Li, L., Cong, P., Wang, Z., Guo, X.: Enhancement of digital radiography image quality using a convolutional neural network. J. X-Ray Sci. Technol. 25(6), 857\u2013868 (2017)","journal-title":"J. X-Ray Sci. Technol."},{"issue":"4","key":"1282_CR29","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1109\/TUFFC.2020.3023154","volume":"68","author":"Y Qi","year":"2020","unstructured":"Qi, Y., Guo, Y., Wang, Y.: Image quality enhancement using a deep neural network for plane wave medical ultrasound imaging. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 68(4), 926\u2013934 (2020)","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"1282_CR30","doi-asserted-by":"crossref","unstructured":"Lefkimmiatis, S.: Non-local color image denoising with convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp.3587\u20133596 (2017)","DOI":"10.1109\/CVPR.2017.623"},{"key":"1282_CR31","doi-asserted-by":"crossref","unstructured":"Yu, S., Park, B., Jeong, J.: Deep iterative down-up cnn for image denoising. In :Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops. (2019)","DOI":"10.1109\/CVPRW.2019.00262"},{"key":"1282_CR32","first-page":"1","volume":"25","author":"J Xie","year":"2012","unstructured":"Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. Adv. Neural. Inf. Process. Syst. 25, 1-9  (2012)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1282_CR33","first-page":"108650","volume":"200","author":"MNS Ou, Yang","year":"2022","unstructured":"Ou, Yang, M.N.S., Swamy, J., Luo, Li, B.: Single image denoising via multi-scale weighted group. sparse coding Signal. Process. 200, 108650 (2022)","journal-title":"Single image denoising via multi-scale weighted group. sparse coding Signal. Process."},{"key":"1282_CR34","doi-asserted-by":"crossref","unstructured":"Lefkimmiatis, S.: Universal denoising networks: a novel CNN architecture for image denoising. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.3204\u20133213 (2018)","DOI":"10.1109\/CVPR.2018.00338"},{"issue":"8","key":"1282_CR35","doi-asserted-by":"publisher","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.: Nonlocality-reinforced convolutional neural networks for image denoising. IEEE. Signal. Process. Lett. 25(8), 1216\u20131220 (2018)","journal-title":"IEEE. Signal. Process. Lett."},{"key":"1282_CR36","doi-asserted-by":"crossref","unstructured":"Gondara, L.: Medical image denoising using convolutional denoising autoencoders. In: IEEE 16th international conference on data mining workshops (ICDMW). pp.241\u2013246 (2016)","DOI":"10.1109\/ICDMW.2016.0041"},{"key":"1282_CR37","doi-asserted-by":"crossref","unstructured":"Chi, J., Zhang, Y., Yu, X., Wang, Y., Wu, C.: Computed tomography (CT) image quality enhancement via a uniform framework integrating noise estimation and super-resolution networks, Sensors.19(15), 3348 (2019)","DOI":"10.3390\/s19153348"},{"key":"1282_CR38","doi-asserted-by":"publisher","first-page":"49720","DOI":"10.1109\/ACCESS.2024.3384577","volume":"12","author":"H Tang","year":"2024","unstructured":"Tang, H., Zhang, W., Zhu, H., Zhao, K.: Self-supervised real-world image denoising based on multi-scale feature enhancement and attention fusion. IEEE Access. 12, 49720\u201349734 (2024)","journal-title":"IEEE Access."},{"key":"1282_CR39","doi-asserted-by":"crossref","unstructured":"Kim, J., Song, S., Yu, S.C.: Denoising auto-encoder based image enhancement for high resolution sonar image, In IEEE underwater technology (UT). 1\u20135 (2017)","DOI":"10.1109\/UT.2017.7890316"},{"issue":"2","key":"1282_CR40","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1109\/TIP.2018.2869685","volume":"28","author":"I Frosio","year":"2018","unstructured":"Frosio, I., Kautz, J.: Statistical nearest neighbors for image denoising. IEEE Trans. Image Process. 28(2), 723\u2013738 (2018)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"1282_CR41","first-page":"167","volume":"2","author":"S Dubey","year":"2012","unstructured":"Dubey, S., Hasan, F., Shrivastava, G.: A hybrid method for image denoising based on wavelet thresholding and RBF network. Int. J. Adv. Comput. Res. 2(2), 167 (2012)","journal-title":"Int. J. Adv. Comput. Res."},{"key":"1282_CR42","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.ins.2013.05.028","volume":"246","author":"XY Wang","year":"2013","unstructured":"Wang, X.Y., Yang, H.Y., Zhang, Y., Fu, Z.K.: Image denoising using SVM classification in nonsubsampled contourlet transform domain. Inf. Sci. 246, 155\u2013176 (2013)","journal-title":"Inf. Sci."},{"issue":"5","key":"1282_CR43","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1109\/TCSVT.2015.2416631","volume":"26","author":"Q Guo","year":"2015","unstructured":"Guo, Q., Zhang, C., Zhang, Y., Liu, H.: An efficient SVD-based method for image denoising. IEEE Trans. Circuits Syst. Video Technol. 26(5), 868\u2013880 (2015)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1282_CR44","doi-asserted-by":"publisher","first-page":"139433","DOI":"10.1109\/ACCESS.2025.3596642","volume":"13","author":"D Jiang","year":"2025","unstructured":"Jiang, D., Chen, Z., Zhang, S., Li, Y., Zhao, L.: Sparse adaptive optimization based on low rank decomposition for image defect detection. IEEE Access. 13, 139433-139444 (2025)","journal-title":"IEEE Access."},{"issue":"37","key":"1282_CR45","doi-asserted-by":"publisher","first-page":"28411","DOI":"10.1007\/s11042-020-09234-5","volume":"79","author":"MR Rejeesh","year":"2020","unstructured":"Rejeesh, M.R., Thejaswini, P.M.O.T.F.: Multi-objective Optimal Trilateral Filtering based partial moving frame algorithm for image denoising. Multimedia Tools Appl. 79(37), 28411\u201328430 (2020)","journal-title":"Multimedia Tools Appl."},{"issue":"10","key":"1282_CR46","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1016\/j.mri.2012.04.005","volume":"30","author":"C Tong","year":"2012","unstructured":"Tong, C., Sun, Y., Payet, N., Ong, S.H.: A general strategy for anisotropic diffusion in MR image denoising and enhancement. Magn. Reson. Imaging. 30(10), 1381\u20131393 (2012)","journal-title":"Magn. Reson. Imaging"},{"key":"1282_CR47","doi-asserted-by":"crossref","unstructured":"Bhutada, G.G., Anand, R.S., Saxena, S.C.: PSO-based learning of sub-band adaptive thresholding function for image denoising, Signal, Image and Video Processing. 6(1), 1\u20137 (2012)","DOI":"10.1007\/s11760-010-0167-7"},{"key":"1282_CR48","doi-asserted-by":"crossref","unstructured":"Liu, X., Cheung, G., Wu, X.: Joint denoising and contrast enhancement of images using graph laplacian operator. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp.2274\u20132278 (2015)","DOI":"10.1109\/ICASSP.2015.7178376"},{"key":"1282_CR49","doi-asserted-by":"publisher","first-page":"145297","DOI":"10.1109\/ACCESS.2020.3015217","volume":"8","author":"Y Guo","year":"2020","unstructured":"Guo, Y., Lu, Y., Liu, R.W., Yang, M., Chui, K.T.: Low-light image enhancement with regularized illumination optimization and deep noise suppression. IEEE Access. 8, 145297\u2013145315 (2020)","journal-title":"IEEE Access."},{"issue":"6","key":"1282_CR50","doi-asserted-by":"publisher","first-page":"2828","DOI":"10.1109\/TIP.2018.2810539","volume":"27","author":"M Li","year":"2018","unstructured":"Li, M., Liu, J., Yang, W., Sun, X., Guo, Z.: Structure-revealing low-light image enhancement via robust retinex model. IEEE Trans. Image Process. 27(6), 2828\u20132841 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"1282_CR51","doi-asserted-by":"crossref","unstructured":"Wang, X.T., Shi, G.M., Niu, Y., Zhang, L.: Robust adaptive directional lifting wavelet transform for image denoising, IET image processing, 5(3), 249\u2013260 (2011)","DOI":"10.1049\/iet-ipr.2009.0112"},{"issue":"5","key":"1282_CR52","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1016\/j.compeleceng.2012.04.003","volume":"39","author":"HS Bhadauria","year":"2013","unstructured":"Bhadauria, H.S., Dewal, M.L.: Medical image denoising using adaptive fusion of curvelet transform and total variation. Comput. Electr. Eng. 39(5), 1451\u20131460 (2013)","journal-title":"Comput. Electr. Eng."},{"issue":"1","key":"1282_CR53","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.dsp.2010.09.002","volume":"21","author":"GG Bhutada","year":"2011","unstructured":"Bhutada, G.G., Anand, R.S., Saxena, S.C.: Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform. Digit. Signal Proc. 21(1), 118\u2013130 (2011)","journal-title":"Digit. Signal Proc."},{"issue":"2","key":"1282_CR54","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s10278-012-9507-1","volume":"26","author":"L Romualdo","year":"2013","unstructured":"Romualdo, L., Vieira, M.A., Schiabel, H., Mascarenhas, N.D., Borges, L.R.: Mammographic image denoising and enhancement using the anscombe transformation, adaptive wiener filtering, and the modulation transfer function. J. Digit. Imaging. 26(2), 183\u2013197 (2013)","journal-title":"J. Digit. Imaging"},{"key":"1282_CR55","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.compeleceng.2019.04.005","volume":"76","author":"C Vimala","year":"2019","unstructured":"Vimala, C., Priya, P.A.: Artificial neural network-based wavelet transform technique for image quality enhancement. Comput. Electr. Eng. 76, 258\u2013267 (2019)","journal-title":"Comput. Electr. Eng."},{"key":"1282_CR56","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.dsp.2017.02.004","volume":"64","author":"HK Rafsanjani","year":"2017","unstructured":"Rafsanjani, H.K., Sedaaghi, M.H., Saryazdi, S.: An adaptive diffusion coefficient selection for image denoising. Digit. Signal Proc. 64, 71\u201382 (2017)","journal-title":"Digit. Signal Proc."},{"key":"1282_CR57","doi-asserted-by":"publisher","unstructured":"Yu, Z., Wang, B., Zeng, P., Zhang, H., Zhang, J., Gao, L., Song, J.: Nicu Sebe, and Heng Tao Shen. A survey on efficient vision-language-action models.  14(8), (2025) https:\/\/doi.org\/10.48550\/arXiv.2510.24795","DOI":"10.48550\/arXiv.2510.24795"},{"issue":"5","key":"1282_CR58","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1109\/TIFS.2015.2398362","volume":"10","author":"W Fan","year":"2015","unstructured":"Fan, W., Wang, K., Cayre, F., Xiong, Z.: Median filtered image quality enhancement and anti-forensics via variational deconvolution. IEEE Trans. Inf. Forensics Secur. 10(5), 1076\u20131091 (2015)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"1282_CR59","doi-asserted-by":"crossref","unstructured":"Gupta, V., Gandhi, D.K., Yadav, P.: Removal of fixed value impulse noise using improved mean filter for image enhancement. In: Nirma University International Conference on Engineering (NUiCONE), IEEE. pp.1\u20135 (2013)","DOI":"10.1109\/NUiCONE.2013.6780117"},{"key":"1282_CR60","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.asoc.2014.11.020","volume":"27","author":"ASA Ghani","year":"2015","unstructured":"Ghani, A.S.A., Isa, N.A.M.: Underwater image quality enhancement through integrated color model with Rayleigh distribution. Appl. Soft Comput. 27, 219\u2013230 (2015)","journal-title":"Appl. Soft Comput."},{"key":"1282_CR61","doi-asserted-by":"crossref","unstructured":"Sood, S., Singh, H., Malarvel, M.: Image quality enhancement for Wheat rust diseased images using Histogram equalization technique, In 5th International Conference on Computing Methodologies and Communication (ICCMC).IEEE. pp.1035\u20131042 (2021)","DOI":"10.1109\/ICCMC51019.2021.9418023"},{"key":"1282_CR62","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1080\/00207160.2017.1401707","volume":"95","author":"A Abirami","year":"2018","unstructured":"Abirami, A., Prakash, P., Thangavel, K.: Fractional diffusion equation-based image denoising model using CN\u2013GL scheme. Int. J. Comput. Math. 95, 6\u20137 (2018)","journal-title":"Int. J. Comput. Math."},{"key":"1282_CR63","doi-asserted-by":"crossref","unstructured":"Yin, W., Lin, X., Sun, Y.: A novel framework for low-light colour image enhancement and denoising, In: 3rd International Conference on Awareness Science and Technology (iCAST), IEEE. pp.20\u201323 (2011)","DOI":"10.1109\/ICAwST.2011.6163088"},{"key":"1282_CR64","doi-asserted-by":"crossref","unstructured":"Li, L., Wang, R., Wang, W., Gao, W.: A low-light image enhancement method for both denoising and contrast enlarging. In: IEEE international conference on image processing (ICIP). pp.3730\u20133734 (2015)","DOI":"10.1109\/ICIP.2015.7351501"},{"key":"1282_CR65","doi-asserted-by":"crossref","unstructured":"Zuo, W., Zhang, L., Song, C., Zhang, D.: Texture enhanced image denoising via gradient histogram preservation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1203\u20131210 (2013)","DOI":"10.1109\/CVPR.2013.159"},{"key":"1282_CR66","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.dsp.2015.09.013","volume":"48","author":"S Tebini","year":"2016","unstructured":"Tebini, S., Mbarki, Z., Seddik, H., Braiek, E.B.: Rapid and efficient image restoration technique based on new adaptive anisotropic diffusion function. Digit. Signal Proc. 48, 201\u2013215 (2016)","journal-title":"Digit. Signal Proc."},{"key":"1282_CR67","doi-asserted-by":"crossref","unstructured":"Alkurt, F.O., Altintas, O., Ozakturk, M., Karaaslan, M., Akgol, O., Unal, E., Sabah, C.: Enhancement of image quality by using metamaterial inspired energy harvester. Phys. Lett. A. 384(1),126041 (2020)","DOI":"10.1016\/j.physleta.2019.126041"},{"key":"1282_CR68","unstructured":"Hu, H., Froment, J., Liu, Q.: Patch-based low-rank minimization for image denoising, arXiv preprint arXiv:1506.08353. 3, (2015)"},{"key":"1282_CR69","doi-asserted-by":"publisher","first-page":"4208114","DOI":"10.1109\/TGRS.2025.3578927","volume":"63","author":"Y Ou","year":"2025","unstructured":"Ou, Y., Esmaeilzehi, A., Omair Ahmad, M., Swamy, M.N.S.: UADiff: A Deep Underwater Image Enhancement Network using Generative Diffusion Prior and Uncertainty-aware Learning. IEEE Trans. Geosci. Remote Sens. 63, 4208114 (2025)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"1282_CR70","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, H., Li, Y., Zhang, H.: A Comprehensive Review of Image Restoration Research Based on Diffusion Models. Mathematics 13(13), 2079 (2025)","DOI":"10.3390\/math13132079"},{"key":"1282_CR71","doi-asserted-by":"crossref","unstructured":"Ali, A.M., Benjdira, B., Koubaa, A., El-Shafai, W.: Zahid Khan, and Wadii Boulila. Vision transformers in image restoration: A survey. Sensors. 23(5), 2385 (2023)","DOI":"10.3390\/s23052385"},{"key":"1282_CR72","doi-asserted-by":"crossref","unstructured":"Wang, H., Yan, X., Hou, X., Zhang, K., Dun, Y.: Extracting noise and darkness: Low-light image enhancement via dual prior guidance. IEEE Trans. Circuits Syst. Video Technol. 32(2), 1700--1714 (2024)","DOI":"10.1109\/TCSVT.2024.3480930"},{"key":"1282_CR73","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, Yang, Y., Xie, H., Yan, H., Zhai, H.: and Binghua Su. Self-supervised Underwater Color Restoration via Wavelet-Diffusion Model with Filtered Multi-Scale Feature Distillation. In: Proceedings of the SIGGRAPH Asia 2025 Conference Papers. pp. 1\u201311 (2025)","DOI":"10.1145\/3757377.3763999"},{"key":"1282_CR74","doi-asserted-by":"crossref","unstructured":"Ou, Y., Wang, Y.: and Yajun Yang. Two-Stage Image Denoising Algorithm Based on Superpixel Nonlocal Group. Circuits, Systems, and Signal Processing. 1\u201318 (2025)","DOI":"10.1007\/s00034-025-03447-5"},{"issue":"2","key":"1282_CR75","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1109\/TBC.2024.3349773","volume":"70","author":"A Esmaeilzehi","year":"2024","unstructured":"Esmaeilzehi, A., Ou, Y., Omair Ahmad, M., Swamy, M.N.S.: DMML: deep multi-prior and multi-discriminator learning for underwater image enhancement. IEEE Trans. Broadcast. 70(2), 637\u2013653 (2024)","journal-title":"IEEE Trans. Broadcast."},{"key":"1282_CR76","doi-asserted-by":"crossref","unstructured":"Wang, R., Jiang, Y., Wang: Y Jiang Visual Comput. 41(12), 9901--9917 (2025)","DOI":"10.1007\/s00371-025-04009-1"},{"issue":"8","key":"1282_CR77","doi-asserted-by":"publisher","first-page":"7093","DOI":"10.1109\/TPAMI.2025.3567814","volume":"47","author":"C Wang","year":"2025","unstructured":"Wang, C., Lin, J., Li, X.: Structural-equation-modeling-based indicator systems for image quality assessment. IEEE Trans. Pattern Anal. Mach. Intell. 47(8), 7093-7107 (2025)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-026-01282-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-026-01282-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-026-01282-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T22:05:41Z","timestamp":1781647541000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-026-01282-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,15]]},"references-count":77,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1282"],"URL":"https:\/\/doi.org\/10.1007\/s44196-026-01282-3","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,15]]},"assertion":[{"value":"6 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This article is a completely original work of its authors; it has not been published before and will not be sent to other publications until the journal\u2019s editorial board decides not to accept it for publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Yes, all authors agreed to publish the paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical Trial Number"}}],"article-number":"197"}}