Weakly-supervised contrastive learning-based implicit degradation modeling for blind image super-resolution

Deep learning-based image super-resolution (SR) methods have attracted growing interest due to their outstanding performance. However, most of these methods assume that the degradations of low-resolution (LR) observations are fixed and known (e.g., bicubic downsampling). This is however not always true in real scenarios. Moreover, the mismatch between the intrinsic degradation of LR images and the assumed degradation generally causes artifacts. Therefore, it is essential to move beyond idealized assumptions and make the SR model adapt to variant degradations. To achieve this goal, we propose a Blind image Super-Resolution approach based on contrastive learning-based Implicit Degradation Modeling (IDMBSR). Since it is challenging to explicitly estimate degradation parameters, a representation is learned from each LR image to model its degradation and differentiate between variant degradations, thereby guiding the subsequent reconstruction to achieve image-specific SR. Since there are no representation labels for LR images, contrastive learning is used to train the attention-enhanced encoder for degradation encoding with the help of degradation parameters for more effective model training. Moreover, to make the reconstruction process adapt to LR images with variant degradations, a degradation-guided SR network is developed, in which the degradation representation adaptively influences the SR process from beginning to end by applying data-driven transformation to middle features. Benefitting from the combination of the degradation representation encoder and the degradation-guided SR network, IDMBSR can adapt to various LR observations without any prior knowledge about their degradations. Experimental results show that IDMBSR outperforms several state-of-the-art blind SR methods with fewer parameters and higher efficiency.