Binarizing Super-Resolution Networks By Pixel-Correlation Knowledge Distillation

Convolutional neural networks (CNNs) have been widely used in single image super-resolution (SR) and obtained remarkable performance. However, most CNN-based SR models require heavy computation, which limits their real-world applications. In this paper, we address the computation problem of SR by network binarization, which converts the full-precision network into the binary network, thus intensively reducing computation. We propose the pixel-correlation distillation for SR network binarization, which distills the knowledge of pixel relationship from the original full-precision network to the binary network. In addition, we further reduce the quantization errors of the binary network by introducing the trainable scaling factors to replace the fixed scaling factors in most existing binarization methods. We carry out extensive experiments on SRResNet and VDSR, which are two commonly used SR networks. It is shown that the proposed method can generate more visually pleasing SR images, and consistently outperform other state-of-the-art methods in PSNR and SSIM.