# Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution

arXiv: https://arxiv.org/abs/1604.08671 · HTML: https://mlsanity.com/paper/1604.08671/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2016.

## Built on by (most cited first, 22 of 22)

- [A Systematic Survey of Deep Learning-based Single-Image Super-Resolution](https://mlsanity.com/paper/2109.14335/) (2021)
- [Structure-Preserving Image Super-Resolution](https://mlsanity.com/paper/2109.12530/) (2021)
- [Semantic Guided Large Scale Factor Remote Sensing Image Super-resolution with Generative Diffusion Prior](https://mlsanity.com/paper/2405.07044/) (2024)
- [Comprehensive Relighting: Generalizable and Consistent Monocular Human Relighting and Harmonization](https://mlsanity.com/paper/2504.03011/) (2025)
- [Cross-SRN: Structure-Preserving Super-Resolution Network with Cross Convolution](https://mlsanity.com/paper/2201.01458/) (2022)
- [DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks](https://mlsanity.com/paper/2112.13191/) (2021)
- [Spatial Attention-based Implicit Neural Representation for Arbitrary Reduction of MRI Slice Spacing](https://mlsanity.com/paper/2205.11346/) (2022)
- [SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Network](https://mlsanity.com/paper/2107.01330/) (2021)
- [Priors in Deep Image Restoration and Enhancement: A Survey](https://mlsanity.com/paper/2206.02070/) (2022)
- [Fast MRI Reconstruction via Edge Attention](https://mlsanity.com/paper/2304.11400/) (2023)
- [Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution](https://mlsanity.com/paper/2206.03361/) (2022)
- [Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution](https://mlsanity.com/paper/2212.01628/) (2022)
- [Kernelized Back-Projection Networks for Blind Super Resolution](https://mlsanity.com/paper/2302.08478/) (2023)
- [Efficient Noise Mitigation for Enhancing Inference Accuracy in DNNs on Mixed-Signal Accelerators](https://mlsanity.com/paper/2409.18553/) (2024)
- [Leveraging Diffusion Knowledge for Generative Image Compression with Fractal Frequency-Aware Band Learning](https://mlsanity.com/paper/2503.11321/) (2025)
- [SONAR: Spectral-Contrastive Audio Residuals for Generalizable Deepfake Detection](https://mlsanity.com/paper/2511.21325/) (2025)
- [ISTA-Inspired Network for Image Super-Resolution](https://mlsanity.com/paper/2210.07818/) (2022)
- [Super-Resolution on Rotationally Scanned Photoacoustic Microscopy Images Incorporating Scanning Prior](https://mlsanity.com/paper/2312.07226/) (2023)
- [Super-Resolution for Remote Sensing Imagery via the Coupling of a Variational Model and Deep Learning](https://mlsanity.com/paper/2412.09841/) (2024)
- [EatGAN: An Edge-Attention Guided Generative Adversarial Network for Single Image Super-Resolution](https://mlsanity.com/paper/2509.14550/) (2025)
- [Rethinking Skip Connections: Additive U-Net for Robust and Interpretable Denoising](https://mlsanity.com/paper/2601.13208/) (2026)
- [MetaSR: Content-Adaptive Metadata Orchestration for Generative Super-Resolution](https://mlsanity.com/paper/2604.26244/) (2026)

## Abstract (arXiv)

In this work, we consider the image super-resolution (SR) problem. The main challenge of image SR is to recover high-frequency details of a low-resolution (LR) image that are important for human perception. To address this essentially ill-posed problem, we introduce a Deep Edge Guided REcurrent rEsidual~(DEGREE) network to progressively recover the high-frequency details. Different from most of existing methods that aim at predicting high-resolution (HR) images directly, DEGREE investigates an alternative route to recover the difference between a pair of LR and HR images by recurrent residual learning. DEGREE further augments the SR process with edge-preserving capability, namely the LR image and its edge map can jointly infer the sharp edge details of the HR image during the recurrent recovery process. To speed up its training convergence rate, by-pass connections across multiple layers of DEGREE are constructed. In addition, we offer an understanding on DEGREE from the view-point of sub-band frequency decomposition on image signal and experimentally demonstrate how DEGREE can recover different frequency bands separately. Extensive experiments on three benchmark datasets clearly demonstrate the superiority of DEGREE over well-established baselines and DEGREE also provides new state-of-the-arts on these datasets.