Zero-Reference Deep Curve Estimation (ZeroDCE) for Low-Light Image Enhancement

PyTorch implementation of Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement Chongyi Li et al.

Execute code

• Copy the https link from GitHub repository.
• Using terminal use git clone in your desired directory.
• Type cd ZeroDCE in terminal.
• Execute test_one.py script to run pretrained model on a random image.
• Execute train.py to train a new model and save it in the models folder.

Main Model Architecture

Complete model which will iteratively apply pixel-wise transformations to an image to enhance it.

CNN Architecture

Loss Functions

• Spatial Consistency Loss
• Exposure Control Loss
• Color Constancy Loss
• Illumination Smoothness Loss

Prerequisites

• Pytorch
• NumPy
• python 3

Some Results

Contributors:

• Ankur Chemburkar
• Devang Jain
• Hashir K