Image denoising algorithm based on adversarial learning using joint loss function

Yongyi Yu; Meng Chang; Huajun Feng; Zhihai Xu; Qi Li; Yueting Chen

doi:10.1117/12.2507518

7 November 2018 Image denoising algorithm based on adversarial learning using joint loss function

Yongyi Yu, Meng Chang, Huajun Feng, Zhihai Xu, Qi Li, Yueting Chen

Proceedings Volume 10832, Fifth Conference on Frontiers in Optical Imaging Technology and Applications; 108320U (2018) https://doi.org/10.1117/12.2507518
Event: Fifth Conference on Frontiers in Optical Imaging Technology and Applications, 2018, Changchun, China

Abstract

A generative adversarial network denoising algorithm which uses a combination of three kinds of loss functions was proposed to avoid the loss of image details in the denoising process. The mean square error loss function was used to make the denoising results similar to the original images, the perceptual loss function was used to understand the image semantic information, and the adversarial learning loss function was used to make images more realistic. The algorithm used the deep residual network, the densely connected convolutional network and a wide and shallow network as the component in the replaceable module of the network. The results show that the three networks tested can make images more detailed and have better peak signal to noise ratio while removing image noise. Among them, the wide and shallow network which uses fewer layers, larger convolution kernels and more feature maps achieves the best result.

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Yongyi Yu, Meng Chang, Huajun Feng, Zhihai Xu, Qi Li, and Yueting Chen "Image denoising algorithm based on adversarial learning using joint loss function", Proc. SPIE 10832, Fifth Conference on Frontiers in Optical Imaging Technology and Applications, 108320U (7 November 2018); https://doi.org/10.1117/12.2507518

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KEYWORDS

Denoising

Image denoising

Image enhancement

Image quality

Convolution

Convolutional neural networks

Image processing

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