mlsanityData to 14 Oct 2026

ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network

arXiv 2001.08073 ↗First posted 21 Jan 2020eess.IV cs.LGcited by 27

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Abstract (arXiv)

Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a perceptual-driven approach for single image super resolution that is able to produce photorealistic images. Despite the visual quality of these generated images, there is still room for improvement. In this fashion, the model is extended to further improve the perceptual quality of the images. We have designed a novel block to replace the one used by the original ESRGAN. Moreover, we introduce noise inputs to the generator network in order to exploit stochastic variation. The resulting images present more realistic textures. The code is available at https://github.com/ncarraz/ESRGANplus .

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Jan 2020.

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Citing papers per year: 2021: 6; 2022: 6; 2023: 4; 2024: 7; 2025: 4.

27 papers.

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  1. 2021Fast and Explicit Neural View Synthesiscited by 28
  2. 2021Noise Conditional Flow Model for Learning the Super-Resolution Spacecited by 19
  3. 2021Variational AutoEncoder for Reference based Image Super-Resolutioncited by 18
  4. 2024Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of Artifactscited by 16
  5. 2022Generative Adversarial Networks for Image Super-Resolution: A Surveycited by 11
  6. 2022FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing Flowcited by 8
  7. 2024Capturing Climatic Variability: Using Deep Learning for Stochastic Downscalingcited by 8
  8. 2021Debiased Subjective Assessment of Real-World Image Enhancementcited by 7
  9. 2022Dual Perceptual Loss for Single Image Super-Resolution Using ESRGANcited by 3
  10. 2024Does Diffusion Beat GAN in Image Super Resolution?cited by 3
  11. 2022Perception-Distortion Trade-off in the SR Space Spanned by Flow Modelscited by 2
  12. 2023GANalyzer: Analysis and Manipulation of GANs Latent Space for Controllable Face Synthesiscited by 2
15 more
  1. 2023StarSRGAN: Improving Real-World Blind Super-Resolutioncited by 2
  2. 2025Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Modelscited by 2
  3. 2021AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucinationcited by 1
  4. 2022Exploiting Digital Surface Models for Inferring Super-Resolution for Remotely Sensed Imagescited by 1
  5. 2024Trustworthy SR: Resolving Ambiguity in Image Super-resolution via Diffusion Models and Human Feedbackcited by 1
  6. 2024Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measurescited by 1
  7. 2024Deep Learning based Optical Image Super-Resolution via Generative Diffusion Models for Layerwise in-situ LPBF Monitoringcited by 1
  8. 2025Binarization-Aware Adjuster for Discrete Decision Learning with an Application to Edge Detectioncited by 1
  9. 2021ATP-Net: An Attention-based Ternary Projection Network For Compressed Sensingcited by 0
  10. 2022Adaptive adversarial training method for improving multi-scale GAN based on generalization bound theorycited by 0
  11. 2023An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognitioncited by 0
  12. 2023Learning-Based and Quality Preserving Super-Resolution of Noisy Imagescited by 0
  13. 2024ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving Transformercited by 0
  14. 2025Involution and BSConv Multi-Depth Distillation Network for Lightweight Image Super-Resolutioncited by 0
  15. 2025SupResDiffGAN a new approach for the Super-Resolution taskcited by 0