Contrastive learning for a single historical painting’s blind super-resolution

Abstract

Most of the existing blind super-resolution(SR) methods explicitly estimate the kernel in pixel space, which usually has a large deviation and results in poor SR performance. As a seminal work, DASR learns abstract representations to distinguish various degradations in the feature space, which effectively reduces degradation estimation bias. Therefore, we also employ the feature space to extract degradation representations for an ancient painting. However, most of the blind SR mehods, including DASR, are committed to removing degradations introduced by kernels, downsampling and additive noise. Among them, downsampling degradation is often accompanied by unpleasant artifacts. To address this issue, the paper designs a high-resolution(HR) representation encoder $E_{HR}$ based on contrastive learning to distinguish artifacts introduced by downsampling. Moreover, to optimize the ill-posed nature of blind SR, we propose a contrastive regularization$\left(CR\right)$ to minimize the contrastive loss based on VGG-19. With the help of $CR$, the SR images are pulled closer to the HR images and pushed far away from bicubic LR observations. Benefiting from these improvements, our method consistently achieves higher quantitative performance and better visual quality with more natural textures than state-of-the-art approaches on a specialized painting dataset.