This is the approximate implementation of the DRHT paper. The project page can be found here:

https://ybsong00.github.io/cvpr18_imgcorrect/index.html

The ldr2hdr model is from HDRCNN http://hdrv.org/hdrcnn/. We will release our original implementation of ldr2hdr soon. The model can be found at either the HDRCNN project page or here:

https://drive.google.com/open?id=138JfKA5QzjDu78PLf6Ih9t5Qh2bBMvhI.

You need to download it at first and put it under checkpoint folder.

The illustration of the files and folders.

############### folders ################

checkpoint --- pre-trained models

input --- input ldr images

hdr_output --- hdr files

samples --- ldr results

############### .py files ################

ldr2hdr.py and hdr2ldr.py define the ldr2hdr and hdr2ldr networks, respectively.

ldr2hdr_test.py and hdr2ldr_test.py provide simple evaluation.

############### notes ################

1. The ldr2hdr part is based on the Siggraph Asia 17 paper "HDR image reconstruction from a single exposure using deep CNNs".

2. The hdr2ldr part performs better when using large batch_size.

If you find the code useful, please cite the following papers:

@inproceedings{yang-cvpr18-DRHT,
    author = {Yang, Xin and Xu, Ke and Song, Yibing and Zhang, Qiang and Wei, Xiaopeng and Rynson, Lau},
    title = {Image Correction via Deep Reciprocating HDR Transformation},
    booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
    year = {2018},
  }

@article{EKDMU17,
  author       = "Eilertsen, Gabriel and Kronander, Joel, and Denes, Gyorgy and Mantiuk, Rafa\l and Unger, Jonas",
  title        = "HDR image reconstruction from a single exposure using deep CNNs",
  journal      = "ACM Transactions on Graphics (TOG)",
  year         = "2017",
}