Deep Learning on Image Denoising: An overview

By Chunwei Tian, Lunke Fei, Wenxian Zheng, Yong Xu, Wangmeng Zuo, Chia-Wen Lin. Neural Networks, 2019

991 citations recorded by the source corpus.

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 analyses. Finally, we point out some potential challenges and directions of future research.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/e7ddb69b872d277f8c69c4c53c5b2e5c49c39aab

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Citations in the corpus

Intermediate Layer Optimization for Inverse Problems using Deep Generative Models (2021, background): “Finally, there is signiﬁcant prior work on deep learning meth-ods that do not rely on pre-trained generators, see e.g. (Lucas et al., 2018; Yu et al., 2019; Liu et al., 2019; Sun & Chen, 2020; Sun et al., 2020; Yang et al., 2019; Tian et al., 2020; Tripathi et al., 2018).”
Multi-Stage Progressive Image Restoration (2021, methodology): “For example, residual learning [30] has been used to perform image denoising [2, 72, 93], image deblurring [42, 43] and image deraining [37].”
Restormer: Efficient Transformer for High-Resolution Image Restoration (2021, methodology): “We refer the reader to NTIRE challenge reports [2,5,30,57] and recent literature reviews [8,42,73], which summarize major design choices for image restoration.”
Artificial intelligence in the creative industries: a review (2020, background): “The survey in [265] states that SDNet [127] achieves the best results on ISO noise, and FFDNet [123] offers the best denoising performance overall, including Gaussian noise and spatially variant noise (non-uniform noise levels).”
SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing (2020, methodology): “In fact, denoising has been a long standing task [19], [20], [21], [22] in image processing while they are rarely designated for object detection, and the denoising is finally performed on raw image for the purpose of image enhancement rather than downstream semantic tasks, especially in an end-to-e”