# Best-Buddy GANs for Highly Detailed Image Super-Resolution

arXiv: https://arxiv.org/abs/2103.15295 · HTML: https://mlsanity.com/paper/2103.15295/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 56 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 30 of 31)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform](https://mlsanity.com/paper/1804.02815/) (2018)
- [Sketch-based Manga Retrieval using Manga109 Dataset](https://mlsanity.com/paper/1510.04389/) (2015)
- [Deeply-Recursive Convolutional Network for Image Super-Resolution](https://mlsanity.com/paper/1511.04491/) (2015)
- [Deep multi-scale video prediction beyond mean square error](https://mlsanity.com/paper/1511.05440/) (2015)
- [MemNet: A Persistent Memory Network for Image Restoration](https://mlsanity.com/paper/1708.02209/) (2017)
- [The relativistic discriminator: a key element missing from standard GAN](https://mlsanity.com/paper/1807.00734/) (2018)
- [Deep Back-Projection Networks For Super-Resolution](https://mlsanity.com/paper/1803.02735/) (2018)
- [Generating Images with Perceptual Similarity Metrics based on Deep Networks](https://mlsanity.com/paper/1602.02644/) (2016)
- [Meta-SR: A Magnification-Arbitrary Network for Super-Resolution](https://mlsanity.com/paper/1903.00875/) (2019)
- [EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis](https://mlsanity.com/paper/1612.07919/) (2016)
- [The 2018 PIRM Challenge on Perceptual Image Super-resolution](https://mlsanity.com/paper/1809.07517/) (2018)
- [RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution](https://mlsanity.com/paper/1908.06382/) (2019)
- [Frequency Separation for Real-World Super-Resolution](https://mlsanity.com/paper/1911.07850/) (2019)
- [MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution](https://mlsanity.com/paper/2007.11803/) (2020)
- [Amortised MAP Inference for Image Super-resolution](https://mlsanity.com/paper/1610.04490/) (2016)
- [A Fully Progressive Approach to Single-Image Super-Resolution](https://mlsanity.com/paper/1804.02900/) (2018)
- [Super-Resolution with Deep Convolutional Sufficient Statistics](https://mlsanity.com/paper/1511.05666/) (2015)
- [Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers](https://mlsanity.com/paper/1912.00157/) (2019)
- [Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination](https://mlsanity.com/paper/1911.03624/) (2019)

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

- [DiffIR: Efficient Diffusion Model for Image Restoration](https://mlsanity.com/paper/2303.09472/) (2023)
- [Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach](https://mlsanity.com/paper/2412.03017/) (2024)
- [VmambaIR: Visual State Space Model for Image Restoration](https://mlsanity.com/paper/2403.11423/) (2024)
- [CoSeR: Bridging Image and Language for Cognitive Super-Resolution](https://mlsanity.com/paper/2311.16512/) (2023)
- [Towards Realistic Data Generation for Real-World Super-Resolution](https://mlsanity.com/paper/2406.07255/) (2024)
- [XPSR: Cross-modal Priors for Diffusion-based Image Super-Resolution](https://mlsanity.com/paper/2403.05049/) (2024)
- [NeRFLiX: High-Quality Neural View Synthesis by Learning a Degradation-Driven Inter-viewpoint MiXer](https://mlsanity.com/paper/2303.06919/) (2023)
- [Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution](https://mlsanity.com/paper/2411.10798/) (2024)
- [Revisiting Temporal Alignment for Video Restoration](https://mlsanity.com/paper/2111.15288/) (2021)
- [DiffI2I: Efficient Diffusion Model for Image-to-Image Translation](https://mlsanity.com/paper/2308.13767/) (2023)
- [Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning](https://mlsanity.com/paper/2403.02601/) (2024)
- [Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation](https://mlsanity.com/paper/2203.10291/) (2022)
- [A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing](https://mlsanity.com/paper/2110.01442/) (2021)
- [Generative Adversarial Networks for Image Super-Resolution: A Survey](https://mlsanity.com/paper/2204.13620/) (2022)
- [Perception-Distortion Balanced Super-Resolution: A Multi-Objective Optimization Perspective](https://mlsanity.com/paper/2312.15408/) (2023)
- [SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolution](https://mlsanity.com/paper/2402.17133/) (2024)
- [DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-Resolution](https://mlsanity.com/paper/2406.16477/) (2024)
- [On the Effectiveness of Spectral Discriminators for Perceptual Quality Improvement](https://mlsanity.com/paper/2307.12027/) (2023)
- [From NeRFLiX to NeRFLiX++: A General NeRF-Agnostic Restorer Paradigm](https://mlsanity.com/paper/2306.06388/) (2023)
- [Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimization](https://mlsanity.com/paper/2201.01034/) (2022)
- [Invertible Sharpening Network for MRI Reconstruction Enhancement](https://mlsanity.com/paper/2206.02838/) (2022)
- [SSL: A Self-similarity Loss for Improving Generative Image Super-resolution](https://mlsanity.com/paper/2408.05713/) (2024)
- [Flow-based Visual Quality Enhancer for Super-resolution Magnetic Resonance Spectroscopic Imaging](https://mlsanity.com/paper/2207.10181/) (2022)
- [Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2403.11078/) (2024)
- [TurboVSR: Fantastic Video Upscalers and Where to Find Them](https://mlsanity.com/paper/2506.23618/) (2025)
- [Super-resolution Reconstruction of Single Image for Latent features](https://mlsanity.com/paper/2211.12845/) (2022)
- [Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures](https://mlsanity.com/paper/2404.06294/) (2024)
- [An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognition](https://mlsanity.com/paper/2310.10022/) (2023)
- [NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Results](https://mlsanity.com/paper/2409.16947/) (2024)
- [AlignVAR: Towards Globally Consistent Visual Autoregression for Image Super-Resolution](https://mlsanity.com/paper/2603.00589/) (2026)

## Abstract (arXiv)

We consider the single image super-resolution (SISR) problem, where a high-resolution (HR) image is generated based on a low-resolution (LR) input. Recently, generative adversarial networks (GANs) become popular to hallucinate details. Most methods along this line rely on a predefined single-LR-single-HR mapping, which is not flexible enough for the SISR task. Also, GAN-generated fake details may often undermine the realism of the whole image. We address these issues by proposing best-buddy GANs (Beby-GAN) for rich-detail SISR. Relaxing the immutable one-to-one constraint, we allow the estimated patches to dynamically seek the best supervision during training, which is beneficial to producing more reasonable details. Besides, we propose a region-aware adversarial learning strategy that directs our model to focus on generating details for textured areas adaptively. Extensive experiments justify the effectiveness of our method. An ultra-high-resolution 4K dataset is also constructed to facilitate future super-resolution research.