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https://github.com/hejingwenhejingwen/csrnet
(ECCV 2020) Conditional Sequential Modulation for Efficient Global Image Retouching
https://github.com/hejingwenhejingwen/csrnet
color-enhancement csrnet deep-learning image-enhancement image-processing photo-retouching
Last synced: 2 months ago
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(ECCV 2020) Conditional Sequential Modulation for Efficient Global Image Retouching
- Host: GitHub
- URL: https://github.com/hejingwenhejingwen/csrnet
- Owner: hejingwenhejingwen
- Created: 2020-07-13T02:40:47.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-09-28T11:07:15.000Z (over 4 years ago)
- Last Synced: 2023-03-05T16:20:18.813Z (almost 2 years ago)
- Topics: color-enhancement, csrnet, deep-learning, image-enhancement, image-processing, photo-retouching
- Language: Python
- Homepage: http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580664.pdf
- Size: 4.58 MB
- Stars: 100
- Watchers: 4
- Forks: 13
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
# Conditional Sequential Modulation for Efficient Global Image Retouching [Paper Link](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580664.pdf)
By Jingwen He*, Yihao Liu*, [Yu Qiao](http://mmlab.siat.ac.cn/yuqiao/), and [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ&hl=en) (* 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](https://www.anaconda.com/download/#linux))
- [PyTorch >= 1.0](https://pytorch.org/)
- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
- 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](https://drive.google.com/drive/folders/1qrGLFzW7RBlBO1FqgrLPrq9p2_p11ZFs?usp=sharing), 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`](codes/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`](experiments/pretrain_models/))
1. Run command:
```c++
python test_CSRNet.py -opt options/test/test_Enhance.yml
```
1. Modify the python file [`calculate_metrics.py`](codes/calculate_metrics.py): `input_path`, `GT_path` (Line 139, 140). Then run:
```c++
python calculate_metrics.py
```
## How to Train
1. Modify the configuration file [`options/train/train_Enhance.yml`](codes/options/train/train_Enhance.yml). e.g., `dataroot_GT`, `dataroot_LQ`.
1. Run command:
```c++
python train.py -opt options/train/train_Enhance.yml
```
## Acknowledgement
- This code is based on [mmsr](https://github.com/open-mmlab/mmsr).
- Thanks Yihao Liu for part of this work.