Research Article
A Review of Convolutional Neural Network based Image Denoising Algorithms
@ARTICLE{10.4108/eetel.v8i3.3461, author={Mengke Wang}, title={A Review of Convolutional Neural Network based Image Denoising Algorithms}, journal={EAI Endorsed Transactions on e-Learning}, volume={8}, number={3}, publisher={EAI}, journal_a={EL}, year={2023}, month={7}, keywords={Convolutional neural network, Denoising Algorithms, Image Processing, Deep Learning}, doi={10.4108/eetel.v8i3.3461} }- Mengke Wang
Year: 2023
A Review of Convolutional Neural Network based Image Denoising Algorithms
EL
EAI
DOI: 10.4108/eetel.v8i3.3461
Abstract
Currently, image-denoising algorithms based on convolutional neural networks (CNN) have been widely used and have achieved good results. Compared with traditional image-denoising methods, it has powerful learning ability and efficient algorithms. This paper summarizes traditional denoising methods and CNN-based image denoising methods, and introduces the basics of image denoising in detail, which is helpful for readers who are starting with image denoising processing. In addition, this paper also summarizes some commonly used datasets in the field of image processing, which makes it easier for us to denoise images. Finally, some suggestions for improving the performance of CNN image denoising are presented, and possible future research directions are discussed.
Copyright © 2023 Wang et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
