In this repository we provide code of the paper:
LUCK: Lighting Up Colors in the Dark
Yaping Zhao, Edmund Y. Lam
This is the Pytorch implementation of our work. The next requirments and some other frequently-used Library will be needed.
- Python >= 3.7
- Pytorch >= 1.7.1
- scikit-image 0.18.1
- imageio 2.9.0
- rawpy 0.17.0
You can simply run the following commands for pre-requisites:
conda env create -f environment.yml
conda activate h4m
We adopt the MCR [Google Drive, Baidu Netdisk (Extraction code: 22cv)], a dataset of colored raw and monochrome raw image pairs, captured with the same exposure setting. Each image has a resolution of 1280×1024.
The zip file contain 3 parts:
- Mono_Colored_RAW_Paired_DATASET
- RGB_GT (498 images)
- Mono_GT (498 images)
- Color_RAW_Input (498 × 8 images)
Totally 498 different scenes, each scene has 1 corresponding RGB and Monochrome ground truth and 8 different exposure color Raw inputs.
(The 8 exposures Monochrome images are available at Google Drive, Badui Netdisk (Extraction code: 22cv) )
The file name contains the image information. Take the image name:"C00001_48mp_0x8_0x1fff.tif" as an example.
"C" means it is color raw image;
"00001" is the image number;
"48mp" is the master clock frequency 48 MHz;
"0x8" is the hex number of global gain;
"0x1fff" indicate the shutter width of the camera which can calculate the exposure time.
The code is a bit messy right now, and I am tidying it up.