The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates 
light enhancement as a task of image-specific curve estimation with a deep network. 
Our method trains a lightweight deep network, DCE-Net, to estimate pixel-wise and 
high-order curves for dynamic range adjustment of a given image. The curve estimation 
is specially designed, considering pixel value range, monotonicity, and differentiability. 
Zero-DCE is appealing in its relaxed assumption on reference images, i.e., it does not 
require any paired or unpaired data during training. This is achieved through a set of 
carefully formulated non-reference loss functions, which implicitly measure the 
enhancement quality and drive the learning of the network. Our method is efficient 
as image enhancement can be achieved by an intuitive and simple nonlinear curve mapping. 
Despite its simplicity, we show that it generalizes well to diverse lighting conditions. 
Extensive experiments on various benchmarks demonstrate the advantages of our method over 
state-of-the-art methods qualitatively and quantitatively. Furthermore, the potential benefits
of our Zero-DCE to face detection in the dark are discussed. We further present an 
accelerated and light version of Zero-DCE, called (Zero-DCE++), that takes advantage 
of a tiny network with just 10K parameters. Zero-DCE++ has a fast inference speed 
(1000/11 FPS on single GPU/CPU for an image with a size of 1200*900*3) while keeping 
the enhancement performance of Zero-DCE.

lol_datasetv2
├── 100.png
├── 101.png
├── 102.png
├── 103.png
├── 109.png
├── 10.png
├── 95.png
├── 96.png
├── 97.png
├── 98.png
├── 99.png
└── 9.png

0 directories, 500 files

$ python train_model.py --help
usage: train_model.py [-h] --dataset_dir DATASET_DIR [--checkpoint_dir CHECKPOINT_DIR] [--model_type MODEL_TYPE] [--IMG_H IMG_H]
                      [--IMG_W IMG_W] [--IMG_C IMG_C] [--batch_size BATCH_SIZE] [--epoch EPOCH] [--learning_rate LEARNING_RATE]
                      [--dataset_split DATASET_SPLIT] [--logdir LOGDIR] [--iteration ITERATION]

Model training scipt for Zero-DCE models

optional arguments:
  -h, --help            show this help message and exit
  --dataset_dir DATASET_DIR
                        Dataset directory
  --checkpoint_dir CHECKPOINT_DIR
                        Checkpoint directory
  --model_type MODEL_TYPE
                        Type of Model.should be any of: ['zero_dce', 'zero_dce_lite']
  --IMG_H IMG_H         Image height
  --IMG_W IMG_W         Image width
  --IMG_C IMG_C         Image channels
  --batch_size BATCH_SIZE
                        Batch size
  --epoch EPOCH         Epochs
  --learning_rate LEARNING_RATE
                        Learning rate
  --dataset_split DATASET_SPLIT
                        Dataset split
  --logdir LOGDIR       Log directory
  --iteration ITERATION
                        Post enhancing iteration

!python train_model.py --dataset_dir lol_datasetv2/ \
                      --model_type zero_dce_lite \
                      --checkpoint_dir Trained_model/ \ 
                      --IMG_H 512 \
                      --IMG_W 512 \
                      --epoch 60 \
                      --batch_size 4 \ 
                      --iteration 6 \

$ python test_model.py --help                                                                                                                    
usage: test_model.py [-h] --model_path MODEL_PATH [--dataset_path DATASET_PATH] [--img_h IMG_H] [--img_w IMG_W] [--save_plot SAVE_PLOT]
                     [--load_random_data LOAD_RANDOM_DATA]

Test model on test dataset

optional arguments:
  -h, --help            show this help message and exit
  --model_path MODEL_PATH
                        path to the saved model folder
  --dataset_path DATASET_PATH
                        path to the dataset
  --img_h IMG_H         image height
  --img_w IMG_W         Image width
  --save_plot SAVE_PLOT
                        save plot of original vs enhanced image. 0: no, 1: yes
  --load_random_data LOAD_RANDOM_DATA
                        load random data. 0: no, 1: yes

!python test_model.py --model_path Trained_model/zero_dce_lite_iter8/zero_dce_lite_200x300_iter8_60/ \
                      --datset_path lol_datasetv2/ \
                      --img_h 200 \
                      --img_w 300 \
                      --save_plot 1 \
                      --load_random_data 0

$ python single_image_enhance.py --help                                                                                      
usage: single_image_enhance.py [-h] --model_path MODEL_PATH --image_path IMAGE_PATH [--img_h IMG_H] [--img_w IMG_W] [--plot PLOT] [--save_result SAVE_RESULT] [--iteration ITERATION]

Single Image Enhancement

optional arguments:
  -h, --help            show this help message and exit
  --model_path MODEL_PATH
                        path to tf model
  --image_path IMAGE_PATH
                        path to image file
  --img_h IMG_H         image height
  --img_w IMG_W         image width
  --plot PLOT           plot enhanced image
  --save_result SAVE_RESULT
                        save enhanced image
  --iteration ITERATION
                        number of Post Ehnancing iterations

$ python single_image_enhance.py --model_path Trained_model/zero_dce_iter6/zero_dce_200x300_iter6_30 \
                                --img_h 200 \
                                --img_w 300 \
                                --image_path sample_images/ low_light_outdoor.jpg \
                                --plot 0 \
                                --save_result 1 \
                                --iteration 6 \

@Article{Zero-DCE,
          author = {Guo, Chunle and Li, Chongyi and Guo, Jichang and Loy, Chen Change and Hou, 
                    Junhui and Kwong, Sam and Cong Runmin},
          title = {Zero-reference deep curve estimation for low-light image enhancement},
          journal = {CVPR},
          pape={1780-1789},
          year = {2020}
    }

@Article{Zero-DCE++,
          author ={Li, Chongyi and Guo, Chunle and Loy, Chen Change},
          title = {Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation},
          journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
          pape={},
          year = {2021},
          doi={10.1109/TPAMI.2021.3063604}
          }

@inproceedings{Chen2018Retinex,

  title={Deep Retinex Decomposition for Low-Light Enhancement},

  author={Chen Wei, Wenjing Wang, Wenhan Yang, Jiaying Liu},

  booktitle={British Machine Vision Conference},

  year={2018},

}