{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:10:32Z","timestamp":1775067032557,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,3,28]],"date-time":"2021-03-28T00:00:00Z","timestamp":1616889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Image denoising is a challenging research problem that aims to recover noise-free images from those that are contaminated with noise. In this paper, we focus on the denoising of images that are contaminated with additive white Gaussian noise. For this purpose, we propose an ensemble learning model that uses the output of three image denoising models, namely ADNet, IRCNN, and DnCNN, in the ratio of 2:3:6, respectively. The first model (ADNet) consists of Convolutional Neural Networks with attention along with median filter layers after every convolutional layer and a dilation rate of 8. In the case of the second model, it is a feed forward denoising CNN or DnCNN with median filter layers after half of the convolutional layers. For the third model, which is Deep CNN Denoiser Prior or IRCNN, the model contains dilated convolutional layers and median filter layers up to the dilated convolutional layers with a dilation rate of 6. By quantitative analysis, we note that our model performs significantly well when tested on the BSD500 and Set12 datasets.<\/jats:p>","DOI":"10.3390\/a14040109","type":"journal-article","created":{"date-parts":[[2021,3,28]],"date-time":"2021-03-28T22:09:01Z","timestamp":1616969341000},"page":"109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Median Filter Aided CNN Based Image Denoising: An Ensemble Approach"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6888-7144","authenticated-orcid":false,"given":"Subhrajit","family":"Dey","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Jadavpur University, Kolkata 700054, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9400-6037","authenticated-orcid":false,"given":"Rajdeep","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jadavpur University, Kolkata 700054, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5118-0812","authenticated-orcid":false,"given":"Friedhelm","family":"Schwenker","sequence":"additional","affiliation":[{"name":"Institute of Neural Information Processing, University of Ulm, 89081 Ulm, Germany"}]},{"given":"Ram","family":"Sarkar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jadavpur University, Kolkata 700054, India"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kortli, Y., Jridi, M., Al Falou, A., and Atri, M. (2020). Face recognition systems: A Survey. Sensors, 20.","DOI":"10.3390\/s20020342"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3529","DOI":"10.1007\/s11042-020-09751-3","article-title":"Understanding contents of filled-in Bangla form images","volume":"80","author":"Bhattacharya","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.neunet.2019.12.024","article-title":"Attention-guided CNN for image denoising","volume":"124","author":"Tian","year":"2020","journal-title":"Neural Netw."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., and Zhang, L. (2017, January 21\u201326). Learning deep CNN denoiser prior for image restoration. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.300"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1109\/TASSP.1986.1164857","article-title":"Nonlinear mean filters in image processing","volume":"34","author":"Pitas","year":"1986","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/S0262-8856(99)00020-7","article-title":"An edge-preserving subband coding model based on non-adaptive and adaptive regularization","volume":"18","author":"Hong","year":"2000","journal-title":"Image Vis. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1093\/biomet\/81.3.425","article-title":"Ideal spatial adaptation by wavelet shrinkage","volume":"81","author":"Donoho","year":"1994","journal-title":"Biometrika"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/83.862633","article-title":"Adaptive wavelet thresholding for image denoising and compression","volume":"9","author":"Chang","year":"2000","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1023\/B:JMIV.0000011321.19549.88","article-title":"An algorithm for total variation minimization and applications","volume":"20","author":"Chambolle","year":"2004","journal-title":"J. Math. Imaging Vis."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","article-title":"Image denoising by sparse 3-D transform-domain collaborative filtering","volume":"16","author":"Dabov","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Schmidt, U., and Roth, S. (2014, January 23\u201328). Shrinkage fields for effective image restoration. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.349"},{"key":"ref_14","unstructured":"Chiang, Y.W., and Sullivan, B. (1989, January 14\u201316). Multi-frame image restoration using a neural network. Proceedings of the IEEE 32nd Midwest Symposium on Circuits and Systems, Champaign, IL, USA."},{"key":"ref_15","unstructured":"Zhou, Y., Chellappa, R., and Jenkins, B. (1987, January 21\u201324). A novel approach to image restoration based on a neural network. Proceedings of the International Conference on Neural Networks, San Diego, CA, USA."},{"key":"ref_16","unstructured":"Mao, X.J., Shen, C., and Yang, Y.B. (2016). Image restoration using convolutional auto-encoders with symmetric skip connections. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., and Fu, Y. (2020). Residual dense network for image restoration. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2020.2968521"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Bottou","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_19","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lefkimmiatis, S. (2017, January 21\u201326). Non-local color image denoising with convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.623"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","article-title":"FFDNet: Toward a fast and flexible solution for CNN-based image denoising","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, J., Chen, J., Chao, H., and Yang, M. (2018, January 18\u201323). Image blind denoising with generative adversarial network based noise modeling. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00333"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Guo, S., Yan, Z., Zhang, K., Zuo, W., and Zhang, L. (2019, January 15\u201320). Toward convolutional blind denoising of real photographs. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00181"},{"key":"ref_26","unstructured":"Liang, L., Deng, S., Gueguen, L., Wei, M., Wu, X., and Qin, J. (2019). Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper Contaminations. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1007\/s12046-017-0654-4","article-title":"Poisson noise reduction from X-ray images by region classification and response median filtering","volume":"42","author":"Kirti","year":"2017","journal-title":"S\u0101dhan\u0101"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.image.2018.06.016","article-title":"Mixed Gaussian-impulse noise reduction from images using convolutional neural network","volume":"68","author":"Islam","year":"2018","journal-title":"Signal Process. Image Commun."},{"key":"ref_29","unstructured":"Martin, D., Fowlkes, C., Tal, D., and Malik, J. (2001, January 7\u201314). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Proceedings of the Eighth IEEE International Conference on Computer Vision. ICCV 2001, Vancouver, BC, Canada."},{"key":"ref_30","first-page":"e453","article-title":"Scikitimage contributors. 2014. scikit-image: Image processing in python","volume":"2","author":"Boulogne","year":"2014","journal-title":"PeerJ"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/4\/109\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:10:16Z","timestamp":1760364616000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/4\/109"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,28]]},"references-count":30,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["a14040109"],"URL":"https:\/\/doi.org\/10.3390\/a14040109","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,28]]}}}