Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance

1. Requirement

The experimental computational device is a PC with an AMD EPYC 7543 32-Core Processor CPU accelerated by an Nvidia A40 GPU, which is also widely used in industrial-grade servers (e.g., Advantech SKY-6000 series and Thinkmate GPX servers).

Put the test images into the input floder
Run test.py
The results will be saved into the output floder.
For the time testing, the inference time is tested by the test code ending time - test code starting time.
To calculate the exact cuda ending time, you should add the 'torch.cuda.synchronize()' before the ending time recorded. (Thanks for the reminder from @CuddleSabe)

The checkpoint and UHD test data are available at: https://pan.baidu.com/s/1LGc7ox7QyLIdEAahmwYtxg Pass code：mipc
Google Drive: https://drive.google.com/file/d/1X18X50iMKfRrGgrr1PT6tE8_ubqTAnpx/view?usp=drive_link
PS: Due to our personal reasons (Graduation and changing computers), I have connected with my coorperator and we can't find the training code. We are sorry about that.

Name		Name	Last commit message	Last commit date
Latest commit History 16 Commits
checkpoints		checkpoints
input		input
output		output
.gitignore		.gitignore
EdgeLoss.py		EdgeLoss.py
README.md		README.md
TVLoss.py		TVLoss.py
Test_SSIM.py		Test_SSIM.py
lap.py		lap.py
makedataset.py		makedataset.py
model.py		model.py
prepare_patches.py		prepare_patches.py
test.py		test.py
utils_train.py		utils_train.py