Official PyTorch implementation of the paper Target-oriented Domain Adaptation for Infrared Image Super-Resolution.
Recent efforts have explored leveraging visible light images to enrich texture details in infrared (IR) super-resolution. However, this direct adaptation approach often becomes a double-edged sword, as it improves texture at the cost of introducing noise and blurring artifacts. To address these challenges, we propose the Target-oriented Domain Adaptation SRGAN (DASRGAN), an innovative framework specifically engineered for robust IR super-resolution model adaptation. DASRGAN operates on the synergy of two key components: 1) Texture-Oriented Adaptation (TOA) to refine texture details meticulously, and 2) Noise-Oriented Adaptation (NOA), dedicated to minimizing noise transfer. Specifically, TOA uniquely integrates a specialized discriminator, incorporating a prior extraction branch, and employs a Sobel-guided adversarial loss to align texture distributions effectively. Concurrently, NOA utilizes a noise adversarial loss to distinctly separate the generative and Gaussian noise pattern distributions during adversarial training. Our extensive experiments confirm DASRGAN's superiority. Comparative analyses against leading methods across multiple benchmarks and upsampling factors reveal that DASRGAN sets new state-of-the-art performance standards.
- Python 3.8, PyTorch >= 1.11
- BasicSR 1.4.2
- Platforms: Ubuntu 18.04, cuda-11
You can also refer to this Log for more info.
Clone the repo
git clone https://github.com/yongsongH/DASRGAN.git
Install dependent packages
cd DASRGAN
pip install -r requirements.txt
Install BasicSR
python setup.py develop
You can also refer to this INSTALL.md for installation