mlsanityData to 7 Oct 2026

AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results

arXiv 1911.01249 ↗First posted 4 Nov 2019eess.IV cs.CVcited by 18

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Abstract (arXiv)

This paper reviews the AIM 2019 challenge on constrained example-based single image super-resolution with focus on proposed solutions and results. The challenge had 3 tracks. Taking the three main aspects (i.e., number of parameters, inference/running time, fidelity (PSNR)) of MSRResNet as the baseline, Track 1 aims to reduce the amount of parameters while being constrained to maintain or improve the running time and the PSNR result, Tracks 2 and 3 aim to optimize running time and PSNR result with constrain of the other two aspects, respectively. Each track had an average of 64 registered participants, and 12 teams submitted the final results. They gauge the state-of-the-art in single image super-resolution.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Nov 2019.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 2; 2024: 4; 2025: 2; 2026: 2.

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  1. 2022Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolutioncited by 96
  2. 2021Fourier Space Losses for Efficient Perceptual Image Super-Resolutioncited by 72
  3. 2023Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolutioncited by 56
  4. 2022NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Resultscited by 40
  5. 2021Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devicescited by 30
  6. 2021Anchor-based Plain Net for Mobile Image Super-Resolutioncited by 24
  7. 2024DVMSR: Distillated Vision Mamba for Efficient Super-Resolutioncited by 23
  8. 2022RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch Normalizationcited by 19
  9. 2021Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolutioncited by 4
  10. 2023Super-Resolution of BVOC Maps by Adapting Deep Learning Methodscited by 3
  11. 2024LIPT: Latency-aware Image Processing Transformercited by 3
  12. 2024Large coordinate kernel attention network for lightweight image super-resolutioncited by 1
6 more
  1. 2024HASN: Hybrid Attention Separable Network for Efficient Image Super-resolutioncited by 1
  2. 2026DualTSR: Unified Dual-Diffusion Transformer for Scene Text Image Super-Resolutioncited by 1
  3. 2021Pyramidal Dense Attention Networks for Lightweight Image Super-Resolutioncited by 0
  4. 2025Efficient Star Distillation Attention Network for Lightweight Image Super-Resolutioncited by 0
  5. 2025Rate-distortion Optimized Point Cloud Preprocessing for Geometry-based Point Cloud Compressioncited by 0
  6. 2026Coupled Continuous-Discrete Generation for Scene Text Image Super-Resolutioncited by 0