# Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training

@article{Wei2021UnsupervisedRI,
title={Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training},
author={Yunxuan Wei and Shuhang Gu and Yawei Li and Longcun Jin},
journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2021},
pages={13380-13389}
}
• Published 2 April 2020
• Computer Science
• 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
These days, unsupervised super-resolution (SR) is soaring due to its practical and promising potential in real scenarios. The philosophy of off-the-shelf approaches lies in the augmentation of unpaired data, i.e. first generating synthetic low-resolution (LR) images ${\mathcal{Y}^g}$ corresponding to real-world high-resolution (HR) images ${\mathcal{X}^r}$ in the real-world LR domain ${\mathcal{Y}^r}$, and then utilizing the pseudo pairs $\left\{ {{\mathcal{Y}^g},{\mathcal{X}^r}} \right\}$ for…
39 Citations

## Figures and Tables from this paper

### Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective

• Computer Science
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
• 2021
A novel unpaired SR training framework based on feature distribution alignment, with which one can obtain degradation-indistinguishable feature maps and then map them to HR images and indicates that this network obtains the state-of-the-art performance over both blind and unpairedSR methods on diverse datasets.

### Real-World Image Super-Resolution by Exclusionary Dual-Learning

• Computer Science
IEEE Transactions on Multimedia
• 2022
A method, Real- World image Super-Resolution by Exclusionary Dual-Learning (RWSR-EDL) to address the feature diversity in perceptual- and L1- based cooperative learning and a noise-guidance data collection strategy is developed to addressed the training time con- sumption in multiple datasets optimization.

### Unpaired Image Super-Resolution with Optimal Transport Maps

• Computer Science
ArXiv
• 2022
An algorithm is proposed for unpaired SR which learns an unbiased OT map for the perceptual transport cost and provides nearly state-of-the-art performance on the large-scale unpaired AIM-19 dataset.

### Frequency Consistent Adaptation for Real World Super Resolution

• Computer Science
AAAI
• 2021
A novel Frequency Consistent Adaptation (FCA) that ensures the frequency domain consistency when applying existing Super-Resolution methods to the real scene and improves the performance of the SR model under real-world setting achieving state-of-the-art results with high fidelity and plausible perception.

### Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations

• Computer Science
ArXiv
• 2022
This paper presents a novel self-supervised learning approach for real-world image SR from observations at dual camera zooms (SelfDZSR), and takes the telephoto image instead of an additional high-resolution image as the supervision information and selects a center patch from it as the reference to super-resolve the corresponding short-focus image patch.

### Learning to Zoom-In via Learning to Zoom-Out: Real-World Super-Resolution by Generating and Adapting Degradation

• Computer Science
IEEE Transactions on Image Processing
• 2021
A framework to learn SR from an arbitrary set of unpaired LR and HR images and to see how far a step can go in such a realistic and “unsupervised” setting is proposed.

### Criteria Comparative Learning for Real-scene Image Super-Resolution

• Computer Science
IEEE Transactions on Circuits and Systems for Video Technology
• 2022
This work proposes a novel training paradigm for RealSR, named Criteria Comparative Learning (Cria-CL), by developing contrastive losses on criteria instead of image patches, and demonstrates that compared with the typical weighted regression strategy, this method achieves a signiﬁcant improvement under similar parameter settings.

### Unpaired Real-World Super-Resolution with Pseudo Controllable Restoration

• Computer Science, Mathematics
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
• 2022
This paper proposes an unpaired real-world super-resolution method that performs on par, or even better than blind paired approaches by introducing a pseudo-controllable restoration module in a fully end-to-end system.

### Kernel Adversarial Learning for Real-world Image Super-resolution

• Computer Science
• 2021
A more realistic process to lower image resolution is proposed by introducing a new Kernel Adversarial Learning Super-resolution (KASR) framework to deal with the real-world image SR problem.

### Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling

• Computer Science
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
• 2021
The hierarchical conditional flow (HCFlow) is proposed as a unified framework for image SR and image rescaling and achieves state-of-the-art performance in terms of both quantitative metrics and visual quality.

## References

SHOWING 1-10 OF 72 REFERENCES

### Unsupervised Learning for Real-World Super-Resolution

• Computer Science
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
• 2019
This work learns to invert the effects of bicubic downsampling in order to restore the natural image characteristics present in the data, and can be trained with direct pixel-wise supervision in the high resolution domain, while robustly generalizing to real input.

### "Zero-Shot" Super-Resolution Using Deep Internal Learning

• Computer Science
CVPR
• 2018
This paper exploits the internal recurrence of information inside a single image, and train a small image-specific CNN at test time, on examples extracted solely from the input image itself, which is the first unsupervised CNN-based SR method.

### Real-World Super-Resolution via Kernel Estimation and Noise Injection

• Computer Science
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
• 2020
This work designs a novel degradation framework for real-world images by estimating various blur kernels as well as real noise distributions and proposes a real- world super-resolution model aiming at better perception.

### Blind Super-Resolution Kernel Estimation using an Internal-GAN

• Computer Science
NeurIPS
• 2019
KernelGAN is introduced, an image-specific Internal-GAN, which trains solely on the LR test image at test time, and learns its internal distribution of patches, and leads to state-of-the-art results in Blind-SR when plugged into existing SR algorithms.

### Kernel Modeling Super-Resolution on Real Low-Resolution Images

• Computer Science
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
• 2019
The proposed KMSR consists of two stages: a pool of realistic blur-kernels with a generative adversarial network (GAN) and then a super-resolution network with HR and corresponding LR images constructed with the generated kernels that incorporates blur-kernel modeling in the training.

### Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks

• Computer Science
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
• 2018
This work proposes a Cycle-in-Cycle network structure with generative adversarial networks (GAN) as the basic component to tackle the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable.

• Computer Science
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
• 2017
This generative adversarial network (GAN)-based method adapts source-domain images to appear as if drawn from the target domain, and outperforms the state-of-the-art on a number of unsupervised domain adaptation scenarios by large margins.

### AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results

• Computer Science
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
• 2019
The AIM 2019 challenge on real world super-resolution addresses the real world setting, where paired true high and low-resolution images are unavailable, and aims to advance the state-of-the-art and provide a standard benchmark for this newly emerging task.

### EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis

• Computer Science
2017 IEEE International Conference on Computer Vision (ICCV)
• 2017
This work proposes a novel application of automated texture synthesis in combination with a perceptual loss focusing on creating realistic textures rather than optimizing for a pixelaccurate reproduction of ground truth images during training to achieve a significant boost in image quality at high magnification ratios.

### Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

• Computer Science
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
• 2017
SRGAN, a generative adversarial network (GAN) for image super-resolution (SR), is presented, to its knowledge, the first framework capable of inferring photo-realistic natural images for 4x upscaling factors and a perceptual loss function which consists of an adversarial loss and a content loss.