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

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 lightweight and highly efficient zero-shot ISR model. The proposed algorithmfirst 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.