mlsanityData to 7 Oct 2026

Best-Buddy GANs for Highly Detailed Image Super-Resolution

arXiv 2103.15295 ↗First posted 29 Mar 2021eess.IV cs.CVcited by 30

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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.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 31 of its 56 references that are in the map. First posted on arXiv 29 Mar 2021.

Builds on

31 of 56 references.

References · 31

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  4. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  5. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
  6. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
  7. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  8. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,199
  9. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  10. 2018Residual Dense Network for Image Super-Resolutioncited by 796
  11. 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 452
  12. 2018Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transformcited by 369
19 more
  1. 2015Sketch-based Manga Retrieval using Manga109 Datasetcited by 326
  2. 2015Deeply-Recursive Convolutional Network for Image Super-Resolutioncited by 317
  3. 2015Deep multi-scale video prediction beyond mean square errorcited by 297
  4. 2017MemNet: A Persistent Memory Network for Image Restorationcited by 244
  5. 2018The relativistic discriminator: a key element missing from standard GANcited by 234
  6. 2018Deep Back-Projection Networks For Super-Resolutioncited by 224
  7. 2016Generating Images with Perceptual Similarity Metrics based on Deep Networkscited by 221
  8. 2019Meta-SR: A Magnification-Arbitrary Network for Super-Resolutioncited by 180
  9. 2016EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesiscited by 177
  10. 2018The 2018 PIRM Challenge on Perceptual Image Super-resolutioncited by 155
  11. 2019RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolutioncited by 145
  12. 2019Frequency Separation for Real-World Super-Resolutioncited by 115
  13. 2020MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolutioncited by 89
  14. 2016Amortised MAP Inference for Image Super-resolutioncited by 68
  15. 2018A Fully Progressive Approach to Single-Image Super-Resolutioncited by 46
  16. 2015Super-Resolution with Deep Convolutional Sufficient Statisticscited by 35
  17. 2019Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolverscited by 35
  18. 2019Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discriminationcited by 32
  19. 2018Maintaining Natural Image Statistics with the Contextual Losscited by 19

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 6; 2023: 8; 2024: 12; 2025: 1; 2026: 1.

30 papers.

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  1. 2023DiffIR: Efficient Diffusion Model for Image Restorationcited by 253
  2. 2024Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approachcited by 93
  3. 2024VmambaIR: Visual State Space Model for Image Restorationcited by 90
  4. 2023CoSeR: Bridging Image and Language for Cognitive Super-Resolutioncited by 66
  5. 2024Towards Realistic Data Generation for Real-World Super-Resolutioncited by 56
  6. 2024XPSR: Cross-modal Priors for Diffusion-based Image Super-Resolutioncited by 41
  7. 2023NeRFLiX: High-Quality Neural View Synthesis by Learning a Degradation-Driven Inter-viewpoint MiXercited by 29
  8. 2024Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolutioncited by 25
  9. 2021Revisiting Temporal Alignment for Video Restorationcited by 24
  10. 2023DiffI2I: Efficient Diffusion Model for Image-to-Image Translationcited by 24
  11. 2024Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learningcited by 19
  12. 2022Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolationcited by 17
18 more
  1. 2021A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensingcited by 13
  2. 2022Generative Adversarial Networks for Image Super-Resolution: A Surveycited by 11
  3. 2023Perception-Distortion Balanced Super-Resolution: A Multi-Objective Optimization Perspectivecited by 11
  4. 2024SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolutioncited by 10
  5. 2024DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-Resolutioncited by 9
  6. 2023On the Effectiveness of Spectral Discriminators for Perceptual Quality Improvementcited by 8
  7. 2022Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimizationcited by 6
  8. 2023From NeRFLiX to NeRFLiX++: A General NeRF-Agnostic Restorer Paradigmcited by 6
  9. 2022Invertible Sharpening Network for MRI Reconstruction Enhancementcited by 5
  10. 2024SSL: A Self-similarity Loss for Improving Generative Image Super-resolutioncited by 5
  11. 2022Flow-based Visual Quality Enhancer for Super-resolution Magnetic Resonance Spectroscopic Imagingcited by 2
  12. 2024Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-Resolutioncited by 2
  13. 2025TurboVSR: Fantastic Video Upscalers and Where to Find Themcited by 2
  14. 2022Super-resolution Reconstruction of Single Image for Latent featurescited by 1
  15. 2024Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measurescited by 1
  16. 2023An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognitioncited by 0
  17. 2024NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Resultscited by 0
  18. 2026AlignVAR: Towards Globally Consistent Visual Autoregression for Image Super-Resolutioncited by 0