Deep neural networks using generative diffusion prior have provided the state-of-the-art performances for the task of blind image super resolution. Thanks to their powerful image generation capability, these deep networks are able to produce high-quality visual signals with realistic textures and structures. However, since these schemes employ a very large number of parameters, their training process is often difficult, and therefore, their performances can be limited. In order to address this, in this paper, we propose a diffusion-based blind image super resolution scheme, which by using a novel learning algorithm with invertible neural networks, is able to provide superior results. Specifically, we argue that because of the reversibility property of invertible neural networks, they are able to generate degraded low-quality images, whose super resolved versions are the upper bound of the image super resolution function space. The inclusion of such visual signals in the training process of our blind image super resolution network leads to facilitating the learning paradigm and achieving higher performances. We show that our proposed blind image super resolution scheme is able to outperform the state-of-the-art methods.
Poudineh et al. (Thu,) studied this question.