Transformer-Progressive Mamba Network for Lightweight Image Super-Resolution

• URL: http://arxiv.org/abs/2511.03232v1
• Date: Wed, 05 Nov 2025 06:46:17 GMT
• Title: Transformer-Progressive Mamba Network for Lightweight Image Super-Resolution
• Authors: Sichen Guo, Wenjie Li, Yuanyang Liu, Guangwei Gao, Jian Yang, Chia-Wen Lin,
• Abstract summary: Mamba-based super-resolution (SR) methods have demonstrated the ability to capture global receptive fields with linear complexity.<n>We propose T-PMambaSR, a lightweight SR framework that integrates window-based self-attention with Progressive Mamba.
• Score: 45.74812546007778
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, Mamba-based super-resolution (SR) methods have demonstrated 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. By enabling interactions among receptive fields of different scales, our method establishes a fine-grained modeling paradigm that progressively enhances feature representation with linear complexity. Furthermore, we introduce an Adaptive High-Frequency Refinement Module (AHFRM) to recover high-frequency details lost during Transformer and Mamba processing. Extensive experiments demonstrate that T-PMambaSR progressively enhances the model's receptive field and expressiveness, yielding better performance than recent Transformer- or Mamba-based methods while incurring lower computational cost. Our codes will be released after acceptance.

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