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

arXiv: https://arxiv.org/abs/1911.01249 · HTML: https://mlsanity.com/paper/1911.01249/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2019.

## Built on by (most cited first, 18 of 18)

- [Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution](https://mlsanity.com/paper/2203.14216/) (2022)
- [Fourier Space Losses for Efficient Perceptual Image Super-Resolution](https://mlsanity.com/paper/2106.00783/) (2021)
- [Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution](https://mlsanity.com/paper/2302.13800/) (2023)
- [NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results](https://mlsanity.com/paper/2205.05675/) (2022)
- [Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices](https://mlsanity.com/paper/2105.10288/) (2021)
- [Anchor-based Plain Net for Mobile Image Super-Resolution](https://mlsanity.com/paper/2105.09750/) (2021)
- [DVMSR: Distillated Vision Mamba for Efficient Super-Resolution](https://mlsanity.com/paper/2405.03008/) (2024)
- [RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch Normalization](https://mlsanity.com/paper/2205.05671/) (2022)
- [Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution](https://mlsanity.com/paper/2103.15290/) (2021)
- [Super-Resolution of BVOC Maps by Adapting Deep Learning Methods](https://mlsanity.com/paper/2302.07570/) (2023)
- [LIPT: Latency-aware Image Processing Transformer](https://mlsanity.com/paper/2404.06075/) (2024)
- [Large coordinate kernel attention network for lightweight image super-resolution](https://mlsanity.com/paper/2405.09353/) (2024)
- [HASN: Hybrid Attention Separable Network for Efficient Image Super-resolution](https://mlsanity.com/paper/2410.09844/) (2024)
- [DualTSR: Unified Dual-Diffusion Transformer for Scene Text Image Super-Resolution](https://mlsanity.com/paper/2603.14207/) (2026)
- [Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2106.06996/) (2021)
- [Efficient Star Distillation Attention Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2506.12475/) (2025)
- [Rate-distortion Optimized Point Cloud Preprocessing for Geometry-based Point Cloud Compression](https://mlsanity.com/paper/2508.01633/) (2025)
- [Coupled Continuous-Discrete Generation for Scene Text Image Super-Resolution](https://mlsanity.com/paper/2608.04525/) (2026)

## 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.