Pushing the Limit of Binarized Neural Network for Image Super Resolution with Smooth Information Transmission

Lightweight models are currently the focal point in image super-resolution (ISR) research, of which the application on resource-limited devices is constrained by heavy computational requirements. As an efficient approach to enhance the inference efficiency of deep learning models, low-bit quantization has garnered significant interest. In this paper, we emphasize that low-bit ISR is not merely a parody of its full-precision version and explore binary quantization in ISR from the perspective of information transmission, pushing the limits of binarized ISR. Specifically, we propose a Maximum Entropy Routing (MER) mechanism to dynamically control activation distribution, maximizing the information entropy of binarized feature maps. Additionally, a Learnable Deviation Compensation (LDC) and an Adaptive Step-size Estimation (ASE) are introduced to reduce information loss during the forward and backward passes, respectively. By enabling smoother information transmission through more flexible binarized activation representations and more precise gradient estimation, the performance gap between binarized and full-precision models is narrowed to less than 0.3 dB. Extensive experiments demonstrate that our proposed binarization method achieves state-of-the-art results in Peak Signal-to-Noise Ratio (PSNR) across all popular benchmarks.