NDLI: Multi-scale Cross-path Concatenation Residual Network for Poisson denoising

Content Provider	IET Digital Library
Author	Su, Yueming Lian, Qiusheng Zhang, Xiaohua Shi, Baoshun Fan, Xiaoyu
Abstract	The signal degradation due to the Poisson noise is a common problem in the low-light imaging field. Recently, deep learning employing the convolution neural network for image denoising has drawn considerable attention owing to its favourable denoising performance. On the basis of the fact that the reconstruction of corrupted pixels can be facilitated by the context information in image denoising, the authors propose a deep multi-scale cross-path concatenation residual network (MC2RNet) which incorporates cross-path concatenation modules for Poisson denoising. Multiple paths are achieved by the cross-path concatenation operation and the skip connection. As a consequence, multi-scale context representations of images under different receptive fields can be learnt by MC2RNet. With the residual learning strategy, MC2RNet learns the residual between the noisy image and the latent clean image rather than the direct mapping to facilitate model training. Specially, unlike existing discriminative Poisson denoising algorithms that train a model only for the specific noise level, they aim to train a single model for handling Poisson noise with different levels, i.e. blind Poisson denoising. Quantitative experiments demonstrate that the proposed model is superior over the state-of-the-art Poisson denoising approaches in terms of peak signal-to-noise ratio and visual effect.
Starting Page	1295
Ending Page	1303
Page Count	9
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 8, Jun (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5941
Journal	IET Image Processing
Publisher Date	2019-02-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Blind Poisson Denoising Computer Vision And Image Processing Technique Convolution Neural Network Convolutional Neural Nets Corrupted Pixel Reconstruction Cross-path Concatenation Module Cross-path Concatenation Operation Deep Learning Deep Multiscale Cross-path Concatenation Residual Network Discriminative Poisson Denoising Algorithm Gaussian Noise Image Denoising Image Reconstruction Image Resolution Knowledge Engineering Technique Latent Clean Image Learning in AI Low-light Imaging Field MC2RNet Multiple Paths Multiscale Context Representations Neural Computing Technique Noisy Image Optical, Image And Video Signal Processing Peak Signal-to-noise Ratio Poisson Noise Receptive Field Residual Learning Strategy Signal Degradation Statistics Stochastic Linearised SCUC Visual Effect
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Combination of multi-scale and residual learning in deep CNN for image denoising

ResDNN: deep residual learning for natural image denoising

Denoising real bursts with squeeze-and-excitation residual network

Enhanced CNN for image denoising

State-of-art analysis of image denoising methods using convolutional neural networks

Chest X-ray image denoising method based on deep convolution neural network

Survey of single image super-resolution reconstruction

Image denoising method based on a deep convolution neural network

DVSNet: deep variance-stabilised network robust to spatially variant characteristics in imaging

Multi-scale Cross-path Concatenation Residual Network for Poisson denoising

Similar Documents

Combination of multi-scale and residual learning in deep CNN for image denoising

ResDNN: deep residual learning for natural image denoising

Denoising real bursts with squeeze-and-excitation residual network

Enhanced CNN for image denoising

State-of-art analysis of image denoising methods using convolutional neural networks

Chest X-ray image denoising method based on deep convolution neural network

Survey of single image super-resolution reconstruction

Image denoising method based on a deep convolution neural network

DVSNet: deep variance-stabilised network robust to spatially variant characteristics in imaging

Multi-scale Cross-path Concatenation Residual Network for Poisson denoising