Deep Learning on Image Denoising: An overview

TL;DR

This paper provides a comprehensive overview and comparison of various deep learning techniques for image denoising, categorizing methods based on noise types and analyzing their motivations, principles, and performance.

Contribution

It offers a systematic classification and comparative analysis of deep CNN-based image denoising methods, highlighting current trends and future challenges.

Findings

Deep CNNs effectively handle different noise types.

State-of-the-art methods outperform traditional approaches.

Identified challenges and future research directions.

Abstract

Deep learning techniques have received much attention in the area of image denoising. However, there are substantial differences in the various types of deep learning methods dealing with image denoising. Specifically, discriminative learning based on deep learning can ably address the issue of Gaussian noise. Optimization models based on deep learning are effective in estimating the real noise. However, there has thus far been little related research to summarize the different deep learning techniques for image denoising. In this paper, we offer a comparative study of deep techniques in image denoising. We first classify the deep convolutional neural networks (CNNs) for additive white noisy images; the deep CNNs for real noisy images; the deep CNNs for blind denoising and the deep CNNs for hybrid noisy images, which represents the combination of noisy, blurred and low-resolution images. Then, we analyze the motivations and principles of the different types of deep learning methods. Next, we compare the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analysis. Finally, we point out some potential challenges and directions of future research.