Learning image block statistics and quality assessment losses for perceptual image super-resolution
Sheng Tian, Lian Zou, Ye Yang, Chuishun Kong, Yifeng Liu · Journal of Electronic Imaging · 2019
The loss function plays an important role in model training for the single-image super-resolution task. Most convolutional neural network-based models adopt conventional pixel-wise loss functions to make impressive advances in peak signal-to-noise ratio and structural similarity index. However, these losses tend to find the average of plausible solutions, which lead to overly smoothed SR results with a lower visual perception. We propose a loss function combining the statistics loss with semantic priors and the quality assessment loss to produce an HR image with high visual quality while maintaining natural image statistics, as perceived by human observers. Our statistics loss measures the similarity of deep feature distributions in different semantic blocks and contributes to the maintenance of natural internal statistics in image restoration. Additionally, a no-reference quality metric that focuses on several aspects of human perceptual preferences for lighting, tone, and sharpness is introduced in our loss function to provide a more visually compelling approximation of human visual perception for perceptual image super-resolution. Experiments prove that our loss function can effectively guide the network to generate images of high-perceptual quality while considering the structural distortion for single-image super-resolution.