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

• URL: http://arxiv.org/abs/2509.06387v1
• Date: Mon, 08 Sep 2025 07:13:58 GMT
• Title: Your Super Resolution Model is not Enough for Tackling Real-World Scenarios
• Authors: Dongsik Yoon, Jongeun Kim,
• Abstract summary: 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.<n>SAAM employs lightweight, scale-adaptive feature extraction and upsampling, incorporating the Simple parameter-free Attention Module (SimAM) for efficient guidance and gradient variance loss.<n>Our method integrates seamlessly into multiple state-of-the-art SR backbones, delivering competitive or superior performance across a wide range of integer and non-integer scale factors.
• Score: 2.101267270902429
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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