Iterative Dual Regression Network for Blind Image Super-resolution

Abstract

Previous single-image super-resolution (SISR) methods assume that the blur kernel is known (e.g.,bicubic) when degrading from high-resolution (HR) images to low-resolution (LR) images. They usea single degradation to train a model to restore HR images. However, the actual degradation inreal-world is often unknown. It is difficult to deal with LR images caused by different degradations.To cope with the above situation, previous methods attempt to restore SR images using a blurkernel estimation structure that combines with a non-blind SR network. There are two problemsthat should be earnestly considered: (1) For accurate blur kernel estimation, insufficient correlation of consecutive kernels lead to an unsatisfied reconstruction result. (2) For ill-posed issue ofimage rebuild, a more efficient constraint condition is worth trying. To solve the two problems, wepropose an iterative dual regression network for an adaptive and precision blur kernel estimation, which improves the speed of kernel estimation by learning a dual mapping. Specifically, we design aPredictor-Generator structure: the Predictor, through several iterations, searching for accurate kernelsthrough intermediate kernels and generated SR images; the Generator, generating final SR images with the help of the predicted kernels. More importantly, the elaborately designed dual learningstrategy can not only provide additional constraints for accurate kernel estimation but also reducethe domain gap between SR images and HR images. Experiments on synthetic degraded imagesand real-world images, our network is competitive in performance and superior in visual results.