Related Experiment Videos
Transformer-Progressive Mamba Network for Lightweight Image Super-Resolution
Summary
T-PMambaSR enhances super-resolution (SR) by integrating window-based self-attention with Progressive Mamba, improving feature representation and detail recovery. This lightweight framework achieves competitive performance with lower computational cost.
Area of Science:
- Computer Vision
- Deep Learning
- Image Processing
Background:
- Transformer-based super-resolution (SR) methods face quadratic computational costs.
- Mamba-based SR methods offer linear complexity but lack fine-grained scale transitions.
- Existing methods struggle with efficient feature representation across different scales.
Purpose of the Study:
- To propose T-PMambaSR, a lightweight SR framework.
- To enhance feature representation through fine-grained scale interactions.
- To recover high-frequency details lost in SR processing.
Main Methods:
- Integration of window-based self-attention with Progressive Mamba.
- Development of an Adaptive High-Frequency Refinement Module (AHFRM).
- Progressive enhancement of model receptive fields and feature expressiveness.
Main Results:
- T-PMambaSR achieves competitive performance compared to Transformer- and Mamba-based SR methods.
- The proposed method demonstrates lower computational cost.
- Effective recovery of high-frequency details is achieved by AHFRM.
Conclusions:
- T-PMambaSR offers an efficient and effective lightweight SR solution.
- The framework successfully addresses limitations in Mamba-based SR methods.
- Fine-grained scale transitions and detail recovery are crucial for advanced SR.
