---
abstract: 'Non-local self-similarity in natural images has been well studied as an effective prior in image restoration. However, for single image super-resolution (SISR), most existing deep non-local methods (e.g., non-local neural networks) only exploit similar patches within the same scale of the low-resolution (LR) input image. Consequently, the restoration is limited to using the same-scale information while neglecting potential high-resolution (HR) cues from other scales. In this paper, we explore the cross-scale patch recurrence property of a natural image, i.e., similar patches tend to recur many times across different scales. This is achieved using a novel cross-scale internal graph neural network ([IGNN]{}). Specifically, we dynamically construct a cross-scale graph by searching $k$-nearest neighboring patches in the downsampled LR image for each query patch in the LR image. We then obtain the corresponding $k$ HR neighboring patches in the LR image and aggregate them adaptively in accordance to the edge label of the constructed graph. In this way, the HR information can be passed from $k$ HR neighboring patches to the LR query patch to help it recover more detailed textures. Besides, these internal image-specific LR/HR exemplars are also significant complements to the external information learned from the training dataset. Extensive experiments demonstrate the effectiveness of [IGNN]{} against the state-of-the-art SISR methods including existing non-local networks on standard benchmarks.'
author:
- |
    Shangchen Zhou$^1$ Jiawei Zhang$^2$ Wangmeng Zuo$^3$ Chen Change Loy$^1$[^1]\
    $^1$Nanyang Technological University $^2$SenseTime Research $^3$Harbin Institute of Technology\
    `{s200094,ccloy}@ntu.edu.sg` `zhangjiawei@sensetime.com` `wmzuo@hit.edu.cn`\
    [<https://github.com/sczhou/IGNN>]{}
bibliography:
- 'references.bib'
title: 'Cross-Scale Internal Graph Neural Network for Image Super-Resolution'
---

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[^1]: Corresponding Author.
