# Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination

arXiv: https://arxiv.org/abs/1911.03624 · HTML: https://mlsanity.com/paper/1911.03624/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2019.

## Built on by (most cited first, 30 of 32)

- [Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution](https://mlsanity.com/paper/2203.09195/) (2022)
- [Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution](https://mlsanity.com/paper/2203.14216/) (2022)
- [Best-Buddy GANs for Highly Detailed Image Super-Resolution](https://mlsanity.com/paper/2103.15295/) (2021)
- [Variational Deep Image Restoration](https://mlsanity.com/paper/2207.01074/) (2022)
- [CaDM: Codec-aware Diffusion Modeling for Neural-enhanced Video Streaming](https://mlsanity.com/paper/2211.08428/) (2022)
- [Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimation](https://mlsanity.com/paper/2211.13676/) (2022)
- [CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution](https://mlsanity.com/paper/2302.12831/) (2023)
- [A Two-Stage Attentive Network for Single Image Super-Resolution](https://mlsanity.com/paper/2104.10488/) (2021)
- [Human Guided Ground-truth Generation for Realistic Image Super-resolution](https://mlsanity.com/paper/2303.13069/) (2023)
- [Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-Resolution](https://mlsanity.com/paper/2304.01994/) (2023)
- [Structure-Preserving Image Super-Resolution](https://mlsanity.com/paper/2109.12530/) (2021)
- [ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution](https://mlsanity.com/paper/2307.00781/) (2023)
- [Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss](https://mlsanity.com/paper/2404.01692/) (2024)
- [DREAM: Diffusion Rectification and Estimation-Adaptive Models](https://mlsanity.com/paper/2312.00210/) (2023)
- [Perception-Distortion Balanced Super-Resolution: A Multi-Objective Optimization Perspective](https://mlsanity.com/paper/2312.15408/) (2023)
- [Low-resolution Human Pose Estimation](https://mlsanity.com/paper/2109.09090/) (2021)
- [TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-Resolution](https://mlsanity.com/paper/2308.06743/) (2023)
- [Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions](https://mlsanity.com/paper/2106.03727/) (2021)
- [Flexible Style Image Super-Resolution using Conditional Objective](https://mlsanity.com/paper/2201.04898/) (2022)
- [Training Generative Adversarial Networks in One Stage](https://mlsanity.com/paper/2103.00430/) (2021)
- [Sobolev Training for Implicit Neural Representations with Approximated Image Derivatives](https://mlsanity.com/paper/2207.10395/) (2022)
- [Manifold Matching via Deep Metric Learning for Generative Modeling](https://mlsanity.com/paper/2106.10777/) (2021)
- [DCS-RISR: Dynamic Channel Splitting for Efficient Real-world Image Super-Resolution](https://mlsanity.com/paper/2212.07613/) (2022)
- [CTSR: Controllable Fidelity-Realness Trade-off Distillation for Real-World Image Super Resolution](https://mlsanity.com/paper/2503.14272/) (2025)
- [On the potential of sequential and non-sequential regression models for Sentinel-1-based biomass prediction in Tanzanian miombo forests](https://mlsanity.com/paper/2106.15020/) (2021)
- [DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution](https://mlsanity.com/paper/2311.18508/) (2023)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)
- [Single Image Super-Resolution](https://mlsanity.com/paper/2101.02802/) (2021)
- [Training Patch Analysis and Mining Skills for Image Restoration Deep Neural Networks](https://mlsanity.com/paper/2207.01075/) (2022)
- [Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target Imputation](https://mlsanity.com/paper/2306.11103/) (2023)

## Abstract (arXiv)

Recently, many convolutional neural networks for single image super-resolution (SISR) have been proposed, which focus on reconstructing the high-resolution images in terms of objective distortion measures. However, the networks trained with objective loss functions generally fail to reconstruct the realistic fine textures and details that are essential for better perceptual quality. Recovering the realistic details remains a challenging problem, and only a few works have been proposed which aim at increasing the perceptual quality by generating enhanced textures. However, the generated fake details often make undesirable artifacts and the overall image looks somewhat unnatural. Therefore, in this paper, we present a new approach to reconstructing realistic super-resolved images with high perceptual quality, while maintaining the naturalness of the result. In particular, we focus on the domain prior properties of SISR problem. Specifically, we define the naturalness prior in the low-level domain and constrain the output image in the natural manifold, which eventually generates more natural and realistic images. Our results show better naturalness compared to the recent super-resolution algorithms including perception-oriented ones.