Learning Omni-Frequency Region-adaptive Representations for Real Image Super-Resolution

Authors

  • Xin Li University of Science and Technology of China
  • Xin Jin University of Science and Technology of China
  • Tao Yu University of Science and Technology of China
  • Simeng Sun University of Science and Technology of China
  • Yingxue Pang University of Science and Technology of China
  • Zhizheng Zhang University of Science and Technology of China
  • Zhibo Chen University of Science and Technology of China

Keywords:

Low Level & Physics-based Vision

Abstract

Traditional single image super-resolution (SISR) methods that focus on solving single and uniform degradation (i.e., bicubic down-sampling), typically suffer from poor performance when applied into real-world low-resolution (LR) images due to the complicated realistic degradations. The key to solving this more challenging real image super-resolution (RealSR) problem lies in learning feature representations that are both informative and content-aware. In this paper, we propose a Omni-frequency Region-adaptive Network (OR-Net) to address both challenges, here we call features of all low, middle and high frequencies omni-frequency features. Specifically, we start from the frequency perspective and design a Frequency Decomposition (FD) module to separate different frequency components to comprehensively compensate the information lost for real LR image. Then, considering the different regions of real LR image have different frequency information lost, we further design a Region-adaptive Frequency Aggregation (RFA) module by leveraging dynamic convolution and spatial attention to adaptively restore frequency components for different regions. The extensive experiments endorse the high-efficient, effective, and scenario-agnostic nature of our OR-Net for RealSR.

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Published

2021-05-18

How to Cite

Li, X., Jin, X., Yu, T., Sun, S., Pang, Y., Zhang, Z., & Chen, Z. (2021). Learning Omni-Frequency Region-adaptive Representations for Real Image Super-Resolution. Proceedings of the AAAI Conference on Artificial Intelligence, 35(3), 1975-1983. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16293

Issue

Section

AAAI Technical Track on Computer Vision II