Dynamic learnable degradation for blind super-resolution

Abstract

Blind super-resolution endeavors to restore clear and high-resolution images from obscure degraded images. To comprehensively encompass the diverse range of real image degradations, prevailing research methods either explicitly or implicitly simulate image degradation. Nevertheless, a majority of prior approaches employ conventional interpolation algorithms for resize operations, thereby impeding the breadth of image degradation diversity. In the case of implicit models, we propose a dynamic learnable degradation (DLD) model, achieved through self-supervised training. This model leverages a dynamic downsampling module to generate a multitude of low-resolution images. By introducing DLD as a novel downsampling method, we devise an original explicit degradation model, which generates additional sets of training image pairs to enhance the practical restoration outcomes of ESRGAN for natural images, called Dynamic-ESRGAN. We also account for the overall degradation and put forth a hybrid-order degradation model to encompass the manifold degradations prevalent in natural images. Exhaustive experimental comparisons conducted on both synthetic and real datasets have unequivocally demonstrated the significant advantages of our DLD and Dynamic-ESRGAN models, both in terms of objective indicators and subjective perception.