Crafting Query-Aware Selective Attention for Single Image Super-Resolution

• URL: http://arxiv.org/abs/2504.06634v1
• Date: Wed, 09 Apr 2025 07:17:29 GMT
• Title: Crafting Query-Aware Selective Attention for Single Image Super-Resolution
• Authors: Junyoung Kim, Youngrok Kim, Siyeol Jung, Donghyun Min,
• Abstract summary: Single Image Super-Resolution (SISR) reconstructs high-resolution images from low-resolution inputs, enhancing image details.<n>We propose SSCAN, which dynamically selects the most relevant key-value windows based on query similarity.<n>Our experiments demonstrate that SSCAN outperforms existing attention-based SISR methods, achieving up to 0.14 dB PSNR improvement on urban datasets.
• Score: 3.133812520659661
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single Image Super-Resolution (SISR) reconstructs high-resolution images from low-resolution inputs, enhancing image details. While Vision Transformer (ViT)-based models improve SISR by capturing long-range dependencies, they suffer from quadratic computational costs or employ selective attention mechanisms that do not explicitly focus on query-relevant regions. Despite these advancements, prior work has overlooked how selective attention mechanisms should be effectively designed for SISR. We propose SSCAN, which dynamically selects the most relevant key-value windows based on query similarity, ensuring focused feature extraction while maintaining efficiency. In contrast to prior approaches that apply attention globally or heuristically, our method introduces a query-aware window selection strategy that better aligns attention computation with important image regions. By incorporating fixed-sized windows, SSCAN reduces memory usage and enforces linear token-to-token complexity, making it scalable for large images. Our experiments demonstrate that SSCAN outperforms existing attention-based SISR methods, achieving up to 0.14 dB PSNR improvement on urban datasets, guaranteeing both computational efficiency and reconstruction quality in SISR.

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