Deep Learning for Image Denoising: A Survey

2018/10/11 by Chunwei Tian, Tian, Chunwei, Yong Xu +5
Computer Science · Engineering · #Advanced Image Fusion Techniques #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Signal Denoising Methods #cs.CV

paper · pdf · doi:10.48550/arxiv.1810.05052

arxiv created 2018/10/11 · openalex publication_date 2018/10/11 · arxiv updated 2018/10/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep learning technology has received a great deal of attention and has been widely applied in the field of imaging processing. In this paper, we have an aim to completely review and summarize the deep learning technologies for image denoising proposed in recent years. Morever, we systematically analyze the conventional machine learning methods for image denoising. Finally, we point out some research directions for the deep learning technologies in image denoising.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Provable Bounds for Learning Some Deep Representations
• Backpropagation Applied to Handwritten Zip Code Recognition
• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• Block-Matching Convolutional Neural Network for Image Denoising
• DiracNets: Training Very Deep Neural Networks Without Skip-Connections
• Residual Dense Network for Image Super-Resolution
• Rich feature hierarchies for accurate object detection and semantic segmentation

Related

• Deep Learning on Image Denoising: An overview
• Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --
• A Comparative Study of Image Denoising Algorithms
• A Comparison of Image Denoising Methods
• Denoising: from classical methods to deep CNNs