IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model
Abstract
Infrared (IR) image super-resolution faces challenges from homogeneous background pixel distributions and sparse target regions, requiring models that effectively handle long-range dependencies and capture detailed local-global information. Recent advancements in Mamba-based (Selective Structured State Space Model) models, employing state space models, have shown significant potential in visual tasks, suggesting their applicability for IR enhancement. In this work, we introduce IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model, a novel Mamba-based model designed specifically for IR image super-resolution. This model enhances the restoration of context-sparse target details through its advanced dependency modeling capabilities. Additionally, a new wavelet transform feature modulation block improves multi-scale receptive field representation, capturing both global and local information efficiently. Comprehensive evaluations confirm that IRSRMamba outperforms existing models on multiple benchmarks. This research advances IR super-resolution and demonstrates the potential of Mamba-based models in IR image processing. Code are available at \url{https://github.com/yongsongH/IRSRMamba}.
- Publication:
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arXiv e-prints
- Pub Date:
- May 2024
- DOI:
- 10.48550/arXiv.2405.09873
- arXiv:
- arXiv:2405.09873
- Bibcode:
- 2024arXiv240509873H
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition;
- Electrical Engineering and Systems Science - Image and Video Processing
- E-Print:
- This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible