Ultralight-weight Binary Neural Network with 1K Parameters for Image Super-Resolution

Image super-resolution (SR) is a long-standing research in the computer vision community, which aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts. The past decade has witnessed impressive advances propelled by deep learning methods. However, the prohibitive model complexity hinders the deployment of deep networks in resource-constrained edge devices. This paper addresses this pain point by pushing the neural architecture to an extremely small size. We comprehensively renovate the modern SR network design including shallow feature extraction, deep feature extraction, and upscale reconstruction, and propose an ultralight-weight binary neural network (UBSR) with only 1K parameters for image SR. Especially, we rethink the design of binary convolution and design an efficient binary convolution block tailored for the SR task. Experimental results show that the proposed method achieves promising performance with desirable parameters and computational overhead. Notably, our UBSR requires only 48M OPs for processing an image and the model parameters are only 1K.