Image Super-Resolution using Explicit Perceptual Loss
This work addresses image quality enhancement in super-resolution, though it appears incremental as it builds on existing perceptual loss methods with improved interpretability.
The paper tackles the problem of generating visually pleasing super-resolution images by proposing an explicit perceptual loss that directly optimizes for perceptual quality, achieving higher perceptual scores than previous approaches.
This paper proposes an explicit way to optimize the super-resolution network for generating visually pleasing images. The previous approaches use several loss functions which is hard to interpret and has the implicit relationships to improve the perceptual score. We show how to exploit the machine learning based model which is directly trained to provide the perceptual score on generated images. It is believed that these models can be used to optimizes the super-resolution network which is easier to interpret. We further analyze the characteristic of the existing loss and our proposed explicit perceptual loss for better interpretation. The experimental results show the explicit approach has a higher perceptual score than other approaches. Finally, we demonstrate the relation of explicit perceptual loss and visually pleasing images using subjective evaluation.