Sparse Convolution Neural Network for Image Denoising
Keywords:
Image Denoising, Convolution Neural Network, Residual Learning, Sparse Representation, Image Reconstruction.Abstract
In this paper, Convolution neural network (CNN) is used to enhance the efficiency and adaptability of image noise reduction. To increase de-noising efficacy, the component enlarges the receiving area of the region employing dilated and traditional convolutions in the sparse representation stage. The feature augmentation phase uses system global and regional features to increase image de-noising interpretation. The Sparse representation comprised of dilated and generalized convolutions is given to improve de-noising acceleration and efficiency. The reconstructive stage is then employed to collect noise information completely. Finally, employing the residue learning approach, this phase manages to generate the noise free image. The result analysis shows the efficiency of proposed framework over existing works.
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Copyright (c) 2022 Dheeraj Kumar Chaturvedi, Saima Khan
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