DeblurGAN-C: image restoration using GAN and a correntropy based loss function in degraded visual environments

Dennis Estrada; Susanne Lee; Fraser Dalgleish; Casey Den Ouden; Madison Young; Caitlin Smith; Joseph Desjardins; Bing Ouyang

doi:10.1117/12.2560792

27 April 2020 DeblurGAN-C: image restoration using GAN and a correntropy based loss function in degraded visual environments

Dennis Estrada, Susanne Lee, Fraser Dalgleish, Casey Den Ouden, Madison Young, Caitlin Smith, Joseph Desjardins, Bing Ouyang

Author Affiliations +

Proceedings Volume 11395, Big Data II: Learning, Analytics, and Applications; 1139507 (2020) https://doi.org/10.1117/12.2560792
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

While machine learning-based image restoration techniques have been the focus in recent years, these algorithms are not adequate to address the effects of a degraded visual environment. An algorithm that successfully mitigates these issues is proposed. The algorithm is built upon the state-of-the-art DeblurGAN algorithm but overcomes several of its deficiencies. The key contributions of the proposed techniques include: 1)Development of an effective framework to generate training datasets typical of a degraded visual environment; 2) Adopting a correntropy based loss function to integrate with the original VGG16 based perceptual loss function and an L1 loss function; 3) Conducting substantial experiments against images from the artificial training datasets and demonstrate the effectiveness of the proposed algorithm.

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Dennis Estrada, Susanne Lee, Fraser Dalgleish, Casey Den Ouden, Madison Young, Caitlin Smith, Joseph Desjardins, and Bing Ouyang "DeblurGAN-C: image restoration using GAN and a correntropy based loss function in degraded visual environments", Proc. SPIE 11395, Big Data II: Learning, Analytics, and Applications, 1139507 (27 April 2020); https://doi.org/10.1117/12.2560792

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