PromptSR: Cascade Prompting for Lightweight Image Super-Resolution

• URL: http://arxiv.org/abs/2507.04118v1
• Date: Sat, 05 Jul 2025 17:56:45 GMT
• Title: PromptSR: Cascade Prompting for Lightweight Image Super-Resolution
• Authors: Wenyang Liu, Chen Cai, Jianjun Gao, Kejun Wu, Yi Wang, Kim-Hui Yap, Lap-Pui Chau,
• Abstract summary: Vision Transformer has significantly advanced image super-resolution (SR)<n>It faces the inherent challenge of a limited receptive field due to the window-based self-attention modeling.<n>We propose PromptSR, a novel prompt-empowered lightweight image SR method.
• Score: 20.796302187697364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although the lightweight Vision Transformer has significantly advanced image super-resolution (SR), it faces the inherent challenge of a limited receptive field due to the window-based self-attention modeling. The quadratic computational complexity relative to window size restricts its ability to use a large window size for expanding the receptive field while maintaining low computational costs. To address this challenge, we propose PromptSR, a novel prompt-empowered lightweight image SR method. The core component is the proposed cascade prompting block (CPB), which enhances global information access and local refinement via three cascaded prompting layers: a global anchor prompting layer (GAPL) and two local prompting layers (LPLs). The GAPL leverages downscaled features as anchors to construct low-dimensional anchor prompts (APs) through cross-scale attention, significantly reducing computational costs. These APs, with enhanced global perception, are then used to provide global prompts, efficiently facilitating long-range token connections. The two LPLs subsequently combine category-based self-attention and window-based self-attention to refine the representation in a coarse-to-fine manner. They leverage attention maps from the GAPL as additional global prompts, enabling them to perceive features globally at different granularities for adaptive local refinement. In this way, the proposed CPB effectively combines global priors and local details, significantly enlarging the receptive field while maintaining the low computational costs of our PromptSR. The experimental results demonstrate the superiority of our method, which outperforms state-of-the-art lightweight SR methods in quantitative, qualitative, and complexity evaluations. Our code will be released at https://github.com/wenyang001/PromptSR.

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