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IRSRMamba

Official PyTorch implementation of the paper IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model.

Introduction

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.

Approach overview

IRSRMamba

Main results

vis

Please check here.

Requirements

Python 3.8, PyTorch >= 1.11

BasicSR 1.4.2

Platforms: Ubuntu 18.04, cuda-11

Installation

Clone the repo

git clone https://github.com/yongsongH/IRSRMamba.git

Install dependent packages

cd IRSRMamba

pip install -r install.txt

Install BasicSR

python setup.py develop

You can also refer to this INSTALL.md for installation

Dataset prepare

Please check this page.

Model

Pre-trained models can be downloaded from this link.

Evaluation

please check the log file for more information about the settings.

Run

  python basicsr/test.py -opt options/test/test_IRSRMamba_SPL_x4.yml

  python basicsr/test.py -opt options/test/test_IRSRMamba_SPL_x2.yml

Contact

If you meet any problems, please describe them and contact me.

Impolite or anonymous emails are not welcome. There may be some difficulties for me to respond to the email without self-introduce. Thank you for understanding.

Acknowledgement

This work is under peer review. The updated manuscript and training dataset will be released after the paper is accepted.