PromptSR: Cascade Prompting for Lightweight Image Super-Resolution
Wenyang Liu, Chen Cai, Jianjun Gao, Kejun Wu, Yi Wang, Kim-Hui Yap, and Lap-Pui Chau

TL;DR
PromptSR introduces a cascade prompting approach that enhances global and local feature integration in lightweight image super-resolution, significantly enlarging receptive fields while maintaining low computational costs.
Contribution
The paper proposes PromptSR with cascade prompting blocks that combine global prompts and local refinement, enabling larger receptive fields efficiently in lightweight SR models.
Findings
Outperforms state-of-the-art lightweight SR methods in accuracy.
Enlarges receptive field without increasing computational complexity.
Demonstrates superior qualitative and quantitative results.
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…
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