Cross-Scale Residual Network: A General Framework for Image Super-Resolution, Denoising, and Deblocking
提出跨尺度残差网络，利用不同尺度间的空间特征和跨时间特征重用，统一处理图像超分辨率、去噪和去块效应三个任务，实验表明在定量和定性评估上优于现有方法。
In general, image restoration involves mapping from low-quality images to their high-quality counterparts. Such optimal mapping is usually nonlinear and learnable by machine learning. Recently, deep convolutional neural networks have proven promising for such learning processing. It is desirable for an image processing network to support well with three vital tasks, namely: 1) super-resolution; 2) denoising; and 3) deblocking. It is commonly recognized that these tasks have strong correlations, which enable us to design a general framework to support all tasks. In particular, the selection of feature scales is known to significantly impact the performance on these tasks. To this end, we propose the cross-scale residual network to exploit scale-related features among the three tasks. The proposed network can extract spatial features across different scales and establish cross-temporal feature reusage, so as to handle different tasks in a general framework. Our experiments show that the proposed approach outperforms state-of-the-art methods in both quantitative and qualitative evaluations for multiple image restoration tasks.