AI RESEARCH
Transformer-Progressive Mamba Network for Lightweight Image Super-Resolution
arXiv CS.CV
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ArXi:2511.03232v2 Announce Type: replace Recently, Mamba-based super-resolution (SR) methods have nstrated the ability to capture global receptive fields with linear complexity, addressing the quadratic computational cost of Transformer-based SR approaches. However, existing Mamba-based methods lack fine-grained transitions across different modeling scales, which limits the efficiency of feature representation. In this paper, we propose T-PMambaSR, a lightweight SR framework that integrates window-based self-attention with Progressive Mamba.