Resolution-Preserving Generative Adversarial Networks for Image Enhancement

Abstract

Generative adversarial networks (GANs) are used for image enhancement such as single image super-resolution (SISR) and deblurring. The conventional GANs-based image enhancement suffers from two drawbacks that cause a quality degradation due to a loss of detailed information. First, the conventional discriminator network adopts strided convolution layers which cause a reduction in the resolution of the feature map, and thereby resulting in a loss of detailed information. Second, the previous GANs for image enhancement use the feature map of the visual geometry group (VGG) network for generating a content loss, which also causes visual artifacts because the maxpooling layers in the VGG network result in a loss of detailed information. To overcome these two drawbacks, this paper presents a proposal of a new resolution-preserving discriminator network architecture which removes the strided convolution layers, and a new content loss generated from the VGG network without maxpooling layers. The proposed discriminator network is applied to the super-resolution generative adversarial network (SRGAN), which is called a resolution-preserving SRGAN (RPSRGAN). Experimental results show that RPSRGAN generates more realistic super-resolution images than SRGAN does, and consequently, RPSRGAN with the new content loss improves the average peak signal-to-noise ratio (PSNR) by 0.75 dB and 0.32 dB for super-resolution images with the scale factors of 2 and 4, respectively. For deblurring, the visual appearance is also significantly improved, and the average PSNR is increased by 1.54 dB when the proposed discriminator and content loss are applied to the deblurring adversarial network.

Keywords

• Single image super-resolution
• deblurring
• generative adversarial networks
• image enhancement