Conditional Sequential Modulation for Efficient Global Image Retouching Paper Link

By Jingwen He*, Yihao Liu*, Yu Qiao, and Chao Dong (* indicates equal contribution)

Left: Compared with existing state-of-the-art methods, our method achieves superior performance with extremely few parameters (1/13 of HDRNet and 1/250 of White-Box). The diameter of the circle represents the amount of trainable parameters. Right: Image retouching examples.

The first row shows smooth transition effects between different styles (expert A to B) by image interpolation. In the second row, we use image interpolation to control the retouching strength from input image to the automatic retouched result. We denote the interpolation coefficient α for each image.

BibTex

@article{he2020conditional,
  title={Conditional Sequential Modulation for Efficient Global Image Retouching},
  author={He, Jingwen and Liu, Yihao and Qiao, Yu and Dong, Chao},
  journal={arXiv preprint arXiv:2009.10390},
  year={2020}
}

Dependencies and Installation

• Python 3 (Recommend to use Anaconda)
• PyTorch >= 1.0
• NVIDIA GPU + CUDA
• Python packages: pip install numpy opencv-python lmdb pyyaml
• TensorBoard:
  • PyTorch >= 1.1: pip install tb-nightly future
  • PyTorch == 1.0: pip install tensorboardX

Datasets

Here, we provide the preprocessed datasets: MIT-Adobe FiveK dataset, which contains both training pairs and testing pairs.

• training pairs: {GT: expert_C_train; Input: raw_input_train}
• testing pairs: {GT: expert_C_test; Input: raw_input_test}

How to Test

1. Modify the configuration file options/test/test_Enhance.yml. e.g., dataroot_GT, dataroot_LQ, and pretrain_model_G. (We provide a pretrained model in experiments/pretrain_models/csrnet.pth)
2. Run command:

python test_CSRNet.py -opt options/test/test_Enhance.yml

1. Modify the python file calculate_metrics.py: input_path, GT_path (Line 139, 140). Then run:

python calculate_metrics.py

How to Train

1. Modify the configuration file options/train/train_Enhance.yml. e.g., dataroot_GT, dataroot_LQ.
2. Run command:

python train.py -opt options/train/train_Enhance.yml

Acknowledgement

• This code is based on mmsr.
• Thanks Yihao Liu for part of this work.