mlsanityData to 14 Oct 2026

StarSRGAN: Improving Real-World Blind Super-Resolution

arXiv 2307.16169 ↗First posted 30 Jul 2023eess.IV cs.CVcited by 2

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Abstract (arXiv)

The aim of blind super-resolution (SR) in computer vision is to improve the resolution of an image without prior knowledge of the degradation process that caused the image to be low-resolution. The State of the Art (SOTA) model Real-ESRGAN has advanced perceptual loss and produced visually compelling outcomes using more complex degradation models to simulate real-world degradations. However, there is still room to improve the super-resolved quality of Real-ESRGAN by implementing recent techniques. This research paper introduces StarSRGAN, a novel GAN model designed for blind super-resolution tasks that utilize 5 various architectures. Our model provides new SOTA performance with roughly 10% better on the MANIQA and AHIQ measures, as demonstrated by experimental comparisons with Real-ESRGAN. In addition, as a compact version, StarSRGAN Lite provides approximately 7.5 times faster reconstruction speed (real-time upsampling from 540p to 4K) but can still keep nearly 90% of image quality, thereby facilitating the development of a real-time SR experience for future research. Our codes are released at https://github.com/kynthesis/StarSRGAN.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 28 references that are in the map. First posted on arXiv 30 Jul 2023.

Builds on

23 of 28 references.

References · 23

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2022Generative Adversarial Networkscited by 5,627
  4. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  5. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  6. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,636
  7. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  8. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,254
  9. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,224
  10. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,204
  11. 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,179
  12. 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 970
11 more
  1. 2022MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessmentcited by 522
  2. 2020A U-Net Based Discriminator for Generative Adversarial Networkscited by 128
  3. 2022Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priorscited by 100
  4. 2022Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolutioncited by 96
  5. 2022Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restorationcited by 73
  6. 2022Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Networkcited by 57
  7. 2021Reflash Dropout in Image Super-Resolutioncited by 41
  8. 2022NTIRE 2022 Challenge on Perceptual Image Quality Assessmentcited by 41
  9. 2020ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Networkcited by 27
  10. 2021A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net Discriminatorscited by 6
  11. 2022Dual Perceptual Loss for Single Image Super-Resolution Using ESRGANcited by 3

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Citing papers per year: 2024: 2.

2 papers.

  1. 2024Using Super-Resolution Imaging for Recognition of Low-Resolution Blurred License Plates: A Comparative Study of Real-ESRGAN, A-ESRGAN, and StarSRGANcited by 0
  2. 2024IG-CFAT: An Improved GAN-Based Framework for Effectively Exploiting Transformers in Real-World Image Super-Resolutioncited by 0