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Cross-Scale Residual Network: A General Framework for Image Super-Resolution, Denoising, and Deblocking. | LitMetric

AI Article Synopsis

  • * The main image processing tasks of interest are super-resolution, denoising, and deblocking, which are closely related and can be tackled within a unified framework.
  • * The proposed cross-scale residual network effectively utilizes features from various scales and promotes the reuse of these features, demonstrating superior performance compared to existing methods in restoring images.

Article Abstract

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.

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Source
http://dx.doi.org/10.1109/TCYB.2020.3044374DOI Listing

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