Résumé: Previous single-image super-resolution methods assume that the blur kernel is known (e.g., bicubic) when degrading from high-resolution (HR) images to low-resolution (LR) images. They use a single degradation to train a model to restore HR images. However, the actual degradation in the real 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 blur kernel estimation structure that combines with a non-blind SR network. There are two problems that 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 of image reconstruction, a more efficient constraint condition is worth trying. To solve the two problems, we propose 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 a Predictor-Generator structure: the Predictor, through several iterations, searching for accurate kernels through 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 learning strategy can not only provide additional constraints for accurate kernel estimation but also reduce the domain gap between SR images and HR images. Experiments on synthetic degraded images and real-world images show that our network is competitive in performance and superior in visual results.

Langue: Anglais