PromptSR: Cascade Prompting for Lightweight Image Super-Resolution
Journal:
arXiv
Published Date:
Jul 5, 2025
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.
