Open AccessResDNN: deep residual learning for natural image denoising
Author(s) -
Singh Gurprem,
Mittal Ajay,
Aggarwal Naveen
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0623
Subject(s) - residual , computer science , artificial intelligence , kernel (algebra) , noise reduction , deep learning , convolution (computer science) , benchmark (surveying) , noise (video) , pattern recognition (psychology) , similarity (geometry) , image (mathematics) , algorithm , artificial neural network , computer vision , mathematics , geodesy , combinatorics , geography
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.