Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning

Xi Cheng, Zhenyong Fu, Jian Yang

Abstract: In the past few years, we have witnessed the great progress of image super-resolution (SR) thanks to the power of deep learning. However, a major limitation of the current image SR approaches is that they assume a pre-determined degradation model or kernel, e.g. bicubic, controls the image degradation process. This makes them easily fail to generalize in a real-world or non-ideal environment since the degradation model of an unseen image may not obey the pre-determined kernel used when training the SR model. In this work, we introduce a simple yet effective zero-shot image super-resolution model. Our zero-shot SR model learns an image-specific super-resolution network (SRN) from a low-resolution input image alone, without relying on external training sets. To circumvent the difficulty caused by the unknown internal degradation model of an image, we propose to learn an image-specific degradation simulation network (DSN) together with our image-specific SRN. Specifically, we exploit the depth information, naturally indicating the scales of local image patches, of an image to extract the unpaired high/low-resolution patch collection to train our networks. According to the benchmark test on four datasets with depth labels or estimated depth maps, our proposed depth guided degradation model learning-based image super-resolution (DGDML-SR) achieves visually pleasing results and can outperform the state-of-the-arts in perceptual metrics.