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