NDLI: Denoising real bursts with squeeze-and-excitation residual network

Content Provider	IET Digital Library
Author	Tan, Hanlin Xiao, Huaxin Lai, Shiming Liu, Yu Zhang, Maojun
Abstract	The goal of image denoising is to recover a clean image from noisy input(s). For single image denoising, utilising similarities (or priors) within and across an image dataset helps recover clean images. As the noise level increases, using multiple frames become feasible, which is defined as burst denoising. In this study, the authors propose a deep residual model with squeeze-and-excitation (SE) modules for the burst denoising. Unlike previous methods, the authors' model does not need an explicit aligning procedure, which is light-weighted and fast. The network contains a noise estimation convolutional neural network, which makes it capable of blind denoising. Besides, by inverting the image processing pipeline and simulating real noise in bursts, their model can suppress real noise blindly. Since denoising performance is closely related to the noise level, frame displacement, and the number of frames (burst length), intensive experiments including ablation study are performed. Quantitative results show that the proposed method performs significantly better than previous state-of-the-art methods V-BM4D and KPN in removing Gaussian noise. Qualitative results show that the proposed method is also effective in removing real noise using bursts and the SE module is key to reduce blur in results.
Starting Page	3095
Ending Page	3104
Page Count	10
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 13, Nov (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0041
Journal	IET Image Processing
Publisher Date	2020-06-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Blind Denoising Burst Denoising Burst Length Computer Vision And Image Processing Technique Convolutional Neural Nets Deep Residual Model Frame Displacement Gaussian Noise Image Dataset Image Denoising Image Processing Pipeline Neural Computing Technique Noise Estimation Convolutional Neural Network Optical, Image And Video Signal Processing Squeeze-and-excitation Module Squeeze-and-excitation Residual Network Statistics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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