# Is Image Super-resolution Helpful for Other Vision Tasks?

arXiv: https://arxiv.org/abs/1509.07009 · HTML: https://mlsanity.com/paper/1509.07009/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2015.

## Built on by (most cited first, 22 of 22)

- [Real-World Single Image Super-Resolution: A Brief Review](https://mlsanity.com/paper/2103.02368/) (2021)
- [Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient Convolution](https://mlsanity.com/paper/2405.07023/) (2024)
- [Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss](https://mlsanity.com/paper/2404.01692/) (2024)
- [A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventions](https://mlsanity.com/paper/2302.04032/) (2023)
- [Impact of deep learning-based image super-resolution on binary signal detection](https://mlsanity.com/paper/2107.02338/) (2021)
- [Exploiting Diffusion Prior for Task-driven Image Restoration](https://mlsanity.com/paper/2507.22459/) (2025)
- [Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection](https://mlsanity.com/paper/2208.03062/) (2022)
- [Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images](https://mlsanity.com/paper/2303.10435/) (2023)
- [Restore Anything Pipeline: Segment Anything Meets Image Restoration](https://mlsanity.com/paper/2305.13093/) (2023)
- [Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module](https://mlsanity.com/paper/2211.09404/) (2022)
- [Unsupervised Variational Translator for Bridging Image Restoration and High-Level Vision Tasks](https://mlsanity.com/paper/2408.08149/) (2024)
- [TaskTok: Delving into Task Tokens for Task-driven Image Restoration](https://mlsanity.com/paper/2606.26615/) (2026)
- [Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data for Aerial Animal Surveillance](https://mlsanity.com/paper/2111.06830/) (2021)
- [A Resolution Enhancement Plug-in for Deformable Registration of Medical Images](https://mlsanity.com/paper/2112.15180/) (2021)
- [Efficient Image Super-Resolution via Symmetric Visual Attention Network](https://mlsanity.com/paper/2401.08913/) (2024)
- [Estimates of maize plant density from UAV RGB images using Faster-RCNN detection model: impact of the spatial resolution](https://mlsanity.com/paper/2105.11857/) (2021)
- [Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images](https://mlsanity.com/paper/2305.19063/) (2023)
- [RestoreDet: Degradation Equivariant Representation for Object Detection in Low Resolution Images](https://mlsanity.com/paper/2201.02314/) (2022)
- [Single Image Super-Resolution Using Lightweight Networks Based on Swin Transformer](https://mlsanity.com/paper/2210.11019/) (2022)
- [Generalized Expectation Maximization Framework for Blind Image Super Resolution](https://mlsanity.com/paper/2305.13880/) (2023)
- [Exploring Linear Attention Alternative for Single Image Super-Resolution](https://mlsanity.com/paper/2502.00404/) (2025)
- [Noise-Free One-Step LoRA for Task-Driven Image Restoration with Diffusion Priors](https://mlsanity.com/paper/2607.25390/) (2026)

## Abstract (arXiv)

Despite the great advances made in the field of image super-resolution (ISR) during the last years, the performance has merely been evaluated perceptually. Thus, it is still unclear whether ISR is helpful for other vision tasks. In this paper, we present the first comprehensive study and analysis of the usefulness of ISR for other vision applications. In particular, six ISR methods are evaluated on four popular vision tasks, namely edge detection, semantic image segmentation, digit recognition, and scene recognition. We show that applying ISR to input images of other vision systems does improve their performance when the input images are of low-resolution. We also study the correlation between four standard perceptual evaluation criteria (namely PSNR, SSIM, IFC, and NQM) and the usefulness of ISR to the vision tasks. Experiments show that they correlate well with each other in general, but perceptual criteria are still not accurate enough to be used as full proxies for the usefulness. We hope this work will inspire the community to evaluate ISR methods also in real vision applications, and to adopt ISR as a pre-processing step of other vision tasks if the resolution of their input images is low.