CVPR 2023 - Super-Resolution Neural Operator

In this episode we discuss Super-Resolution Neural Operator by Min Wei, Xuesong Zhang. The paper proposes a deep learning framework called Super-resolution Neural Operator (SRNO) that can generate high-resolution images from their low-resolution counterparts. It works by learning the mapping between the function spaces of the LR and HR image pairs, embedding the LR input into a higher-dimensional latent representation space, iteratively approximating the implicit image function with kernel integral mechanisms, and generating the RGB representation at the target coordinates. The SRNO outperforms existing continuous SR methods in terms of both accuracy and running time.