NDLI: ResDNN: deep residual learning for natural image denoising

Content Provider	IET Digital Library
Author	Singh, Gurprem Mittal, Ajay Aggarwal, Naveen
Abstract	Image denoising is a thoroughly studied research problem in the areas of image processing and computer vision. In this work, a deep convolution neural network with added benefits of residual learning for image denoising is proposed. The network is composed of convolution layers and ResNet blocks along with rectified linear unit activation functions. The network is capable of learning end-to-end mappings from noise distorted images to restored cleaner versions. The deeper networks tend to be challenging to train and often are posed with the problem of vanishing gradients. The residual learning and orthogonal kernel initialisation keep the gradients in check. The skip connections in the ResNet blocks pass on the learned abstractions further down the network in the forward pass, thus achieving better results. With a single model, one can tackle different levels of Gaussian noise efficiently. The experiments conducted on the benchmark datasets prove that the proposed model obtains a significant improvement in structural similarity index than the previously existing state-of-the-art techniques.
Starting Page	2425
Ending Page	2434
Page Count	10
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 11, Sep (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0623
Journal	IET Image Processing
Publisher Date	2020-04-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Added Benefits Computer Vision Computer Vision And Image Processing Technique Convolution Convolution Layer Deep Convolution Neural Network Deep Residual Learning Deeper Network End-to-end Mappings Gaussian Noise Image Denoising Image Processing Knowledge Engineering Technique Learned Abstractions Learning in AI Natural Image Denoising Neural Computing Technique Neural Nets Noise Distorted Image Optical, Image And Video Signal Processing Rectified Linear Unit ResNet Blocks Statistics Thoroughly Studied Research Problem
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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