What type of study is this?

This is a Quantitative Study study.

October 3, 2025Open Access

Your Super Resolution Model is not Enough for Tackling Real-World Scenarios

DYDong Hyun YoonWaseda University JKJ. KimYonsei University

Key Points

The scale-aware attention module significantly improves single image super-resolution performance across various scale factors.
Experiments reveal the method maintains competitive performance while minimizing computational overhead in robust multi-scale upscaling.
By integrating with state-of-the-art super-resolution backbones like SCNet and HiT-SR, the approach adapts to real-world scenarios effectively.
This research provides a practical solution to the challenges of traditional super-resolution models, enhancing real-world applicability.

Abstract

Despite remarkable progress in Single Image Super-Resolution (SISR), traditional models often struggle to generalize across varying scale factors, limiting their real-world applicability. To address this, we propose a plug-in Scale-Aware Attention Module (SAAM) designed to retrofit modern fixed-scale SR models with the ability to perform arbitrary-scale SR. SAAM employs lightweight, scale-adaptive feature extraction and upsampling, incorporating the Simple parameter-free Attention Module (SimAM) for efficient guidance and gradient variance loss to enhance sharpness in image details. Our method integrates seamlessly into multiple state-of-the-art SR backbones (e.g., SCNet, HiT-SR, OverNet), delivering competitive or superior performance across a wide range of integer and non-integer scale factors. Extensive experiments on benchmark datasets demonstrate that our approach enables robust multi-scale upscaling with minimal computational overhead, offering a practical solution for real-world scenarios.

Ask AI

Helpful

Bookmark

View Full Paper