DeblurGAN-C: image restoration using GAN and a correntropy based loss function in degraded visual environments
Dennis Estrada, Susanne Lee, Fraser R. Dalgleish, Casey Den Ouden, Madison Young, Caitlin Smith, Joseph Desjardins, Bing Ouyang · 2020
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.