Image Denoising Using Deep CGAN With Bi-Skip Connections

Peng Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


With the rapid development of neural networks, many deep learning-based image processing tasks have shown outstanding performance. In this paper, we describe a unified deep learning-based approach for image image denoising. The proposed method is composed of deep convolutional neural and conditional generative adversarial networks. For the discriminator network, we present a new network architecture with bi-skip connections to address hard training and details losing issues. In the generative network, a objective optimization is derived to solve the problem of common conditions being non-identical. Through extensive experiments on image denoising task on both qualitative and quantitative criteria, we demonstrate that our proposed method performs favorably against current state-of-the-art approaches.

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[bibtex]
@InProceedings{Wang_2019_CVPR_Workshops,
author = {Wang, Peng},
title = {Image Denoising Using Deep CGAN With Bi-Skip Connections},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}