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Optimal LED Spectral Multiplexing for NIR2RGB Translation

Lei Lui, Yuze Chen, Junchi Yan, Yinqiang Zheng

This paper has been accpected by CVPR2022. In this short tutorial, we will guide you through setting up the system environment for running the code, which used for NIR-to-RGB translation.

Requirments

• Ubuntu 16.04
• CUDA 9.1
• pytorch 1.7.1

Datasets

1. We have released our hyperspectral images dataset IDH, the wavelength range from 420nm to 1000nm with 10nm intervals.

2. If you only want to go through our model, we suggest to download the processed Dataset and unzip it into datasets/. More details see datasets/readme.txt.

Target Loss Minimization (TLM)

Training phase

1. Take ICVL for example:

   python train.py --dataroot path/to/the/datasets/icvl/train --name experiment_name
   

2. On training image outputs and model are stored in checkpoints/experiment_name, if you have multi GPUs, using --gpu_ids 0 to specify the gpu you want to use.

Testing phase

1. First, make sure that the data in datasets/icvl/test folder is avaliable.

2. Pretrained model:

3. After the training step, or download the pretrained model and put them in checkpoints/experiment_name folder. Run the following command to translate NIR images to RGB images:

   python test.py --dataroot path/to/the/datasets/icvl/test --name experiment_name
   

The results are stored in results/experiment_name folder.

RGB Variance Maximization (RVM)

Run the following command to see the results of RVM with 3 cameras: python util/rvm.py