---
# System prepended metadata

title: ZeroDCE Note
tags: [AI Application]

---

# ZeroDCE

> * Reference \:
> Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement https://github.com/Li-Chongyi/Zero-DCE

## Introduction

![](https://hackmd.io/_uploads/HJfUpwjx0.png)

ZeroDCE generate a set of curve adjustment parameter to adjust lighting condition of an image. This method can avoid creating artifact or generate overexposure or underexposure region.

## Curve Design

* The curve can be iteratively applied to approximate higher-order curves for more robust and accurate dynamic range adjustment.
* The formula for the curve is designed to be differentiable and monotonous.
* The output image will be reiterated for model training
* Iteratively enhance formula: 𝐿𝐸_𝑛 (𝑥)=𝐿𝐸_(𝑛−1) (𝑥)+𝒜_𝑛 𝐿𝐸_(𝑛−1) (𝑥)(1−𝐿𝐸_(𝑛−1) (𝑥))
* 𝒜←\[−1, 1\], 𝐼(𝑥)←\[0, 1\],𝑛=8
	* To avoid overflow truncation, the input pixel value of the enhanced image should be in the normalized rage of \[0, 1\]

![image](https://hackmd.io/_uploads/Sy3CavjgR.png)

## Loss Functions

### Spatial Consistency Loss

![image](https://hackmd.io/_uploads/Bkk2JiieC.png)

### Exposure Control Loss

![image](https://hackmd.io/_uploads/Bk90JijlC.png)

### Color Constancy Loss

![image](https://hackmd.io/_uploads/HJllgsog0.png)

### Color Correctness Loss

![image](https://hackmd.io/_uploads/BJ3EgssgR.png)

### Color Fine Tune Loss

![image](https://hackmd.io/_uploads/BJcTgosxC.png)

### Illumination Smoothness Loss

![image](https://hackmd.io/_uploads/Syc8esjlA.png)

## Result \(After Quantized\)

![](https://hackmd.io/_uploads/ByzjbjjeR.png)

![](https://hackmd.io/_uploads/SJuCfjigA.png)



