# Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution

arXiv: https://arxiv.org/abs/2012.06131 · HTML: https://mlsanity.com/paper/2012.06131/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 15 of 15)

- [Diffusion Models for Image Restoration and Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2308.09388/) (2023)
- [Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2203.00259/) (2022)
- [Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient Convolution](https://mlsanity.com/paper/2405.07023/) (2024)
- [Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution](https://mlsanity.com/paper/2208.03012/) (2022)
- [HST: Hierarchical Swin Transformer for Compressed Image Super-resolution](https://mlsanity.com/paper/2208.09885/) (2022)
- [Multi-scale frequency separation network for image deblurring](https://mlsanity.com/paper/2206.00798/) (2022)
- [Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective](https://mlsanity.com/paper/2303.06859/) (2023)
- [MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMs](https://mlsanity.com/paper/2408.11758/) (2024)
- [Learning from Multi-Perception Features for Real-Word Image Super-resolution](https://mlsanity.com/paper/2305.18547/) (2023)
- [4K-Resolution Photo Exposure Correction at 125 FPS with ~8K Parameters](https://mlsanity.com/paper/2311.08759/) (2023)
- [Toward Real-World Light Field Super-Resolution](https://mlsanity.com/paper/2305.18994/) (2023)
- [End-to-End Optimized Image Compression with the Frequency-Oriented Transform](https://mlsanity.com/paper/2401.08194/) (2024)
- [A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective](https://mlsanity.com/paper/2111.13078/) (2021)
- [Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolution](https://mlsanity.com/paper/2212.14046/) (2022)
- [Hierarchical Frequency-based Upsampling and Refining for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2403.11556/) (2024)

## Abstract (arXiv)

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 an Omni-frequency Region-adaptive Network (ORNet) 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 effective, and scenario-agnostic nature of our OR-Net for RealSR.