Deep High Dynamic Range Imaging Benchmark

by Python awesome

Deep High Dynamic Range Imaging Benchmark

This repository is the pytorch implementation of various High Dynamic Range (HDR) Imaging algorithms. Please find the details below.

Maintenance and Contributors

@TianhongDai and @WeiLi-THU

Requirements

  • pytorch==1.4.0
  • opencv-python
  • scikit-image==0.17.2

ToDo List

  • adaptive padding
  • add more baselines

Supported Algorthms

  • DeepHDR [1]
  • NHDRRNet [2]
  • AHDR [3]
  • DAHDR [4]

Instruction

  1. download the Kalantari dataset via: [link], and organize the dataset as follows:

dataset
│
└───Traning
│   │  001
│   │  002
│   │  003
│   |  ...
│   
└───Test
    │  001
    │  002
    |  003
    |  ...
  1. train the network [unet|nhdrrnet|ahdr|dahdr]:
python train.py --net-type unet --cuda --batch-size 8 --lr 0.0002
  1. continue training using the pre-saved checkpoint:
python train.py --net-type unet --cuda --resume --last-ckpt-path <the saved ckpt path>
  1. test the model and save HDR images:
python eval_metric.py --net-type unet --model-path <the saved ckpt path> --cuda --save-image

Pre-trained Models

The pre-trained models can be downloaded from the released page.

Performance

DeepHDR[1] NHDRRNet[2] AHDR[3] DAHDR[4]
PSNR-$\mu$ 42.2695 42.4769 43.5742 43.5240
SSIM-$\mu$ 0.9941 0.9942 0.9956 0.9956
PSNR-L 40.0627 40.1978 41.1551 40.7534
SSIM-L 0.9892 0.9889 0.9903 0.9905

Acknowledgements

@elliottwu for DeepHDR
@qingsenyangit for AHDRNet
@Galaxies99 for NHDRRNet details

References

[1] Deep High Dynamic Range Imaging with Large Foreground Motions
[2] Deep HDR Imaging via A Non-Local Network
[3] Attention-guided Network for Ghost-free High Dynamic Range Imaging
[4] Dual-Attention-Guided Network for Ghost-Free High Dynamic Range Imaging

GitHub

View Github