ISSR-DIL: Image Specific Super-Resolution Using Deep Identity Learning

Sree Rama Vamsidhar S, Jayadeep D, Rama Krishna Gorthi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 6076-6085

The advent of Deep Learning (DL) techniques has significantly improved the performance of Image Super-Resolution (ISR) algorithms. However the primary limitation to extending the existing DL-based works for real-world instances is their computational and time complexities. Besides this the presumed degradation process in their training datasets is another. In this paper we present a highly efficient zero-shot ISR model. The proposed algorithm first estimates the degradation kernel (K) from the given low-resolution (LR) image statistics. Later we introduce "Deep Identity Learning (DIL)" a novel learning strategy to compute the inverse of K by exploiting the identity relation between the degradation and inverse degradation models. Contrary to the mainstream ISR works the proposed model considers K alone as its input to learn the ISR task. We term the proposed approach as "Image Specific Super-Resolution Using Deep Identity Learning (ISSR-DIL)". In our experiments ISSR-DIL demonstrated a competitive performance compared to state-of-the-art (SotA) works on benchmark ISR datasets while requiring at least by order of 10 fewer computational resources.