Improved retinex low light image enhancement method

Hui HUANG; Lin-lu DONG; Xiao-fang LIU; Liang-jun ZHAO

doi:10.3788/OPE.20202808.1835

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Improved retinex low light image enhancement method

Information Sciences | 更新时间：2020-08-14

- Improved retinex low light image enhancement method
- Optics and Precision Engineering Vol. 28, Issue 8, Pages: 1835-1849(2020)
- 作者机构：
  
  1.人工智能四川省重点实验室, 自贡 643000
  2.四川轻化工大学自动化与信息工程学院, 自贡 643000
  3.四川轻化工大学计算机科学与工程学院, 自贡 643000
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20202808.1835
  CLC： TP391
- Received：31 March 2020，
  
  Revised：24 April 2020，
  
  Accepted：24 April 2020，
  
  Published：25 August 2020
- 稿件说明：
移动端阅览
Hui HUANG, Lin-lu DONG, Xiao-fang LIU, et al. Improved retinex low light image enhancement method[J]. Optics and precision engineering, 2020, 28(8): 1835-1849.
DOI：

Hui HUANG, Lin-lu DONG, Xiao-fang LIU, et al. Improved retinex low light image enhancement method[J]. Optics and precision engineering, 2020, 28(8): 1835-1849. DOI： 10.3788/OPE.20202808.1835.

摘要

针对低光照条件下拍摄的图像受可见光低和噪声的影响，不仅降低图像在视觉上的美感而且会造成重要信息丢失的问题。本文提出结合平滑聚类和改进Retinex算法的估计照明图的低光照图像增强方法。使用平滑聚类将图像分离为细节层和基础层；利用max-RGB找到各通道最大值用于估计每个像素的照度，构建初始照明图，根据局部一致性和交替方向最小化技术优化照明图；自适应Gamma矫正对优化后的照明图进行非线性重标形成最终光照图；根据最终光照图增强输入图像，将增强后图像与细节层进行融合，获得清晰且细节更为丰富的图像；通过与LE，GC，HE，SSR，MSR，MSRCR，MSRCP算法相比，在图像HightB上，边缘强度最高达到1.00e+02，平均梯度最高达到10.520 6，空间频率最高达到52.050 8，图像清晰度最高达到14.656 2，在主观评价和客观评价均优于其他对比算法。实验结果表明，所提算法具有良好的清晰度，更好的保留边缘和细节纹理，使用本文算法增强后的图片质量更高，细节更加丰富。

Abstract

Images taken under low-light conditions are affected by low visible light and noise

which reduce the visual quality and also result in loss of important information.This article proposed a low light image enhancement method that combined smooth clustering and the improved Retinex algorithm to estimate images taken under low-light conditions. An image was separated into the detail layer and the base layer via smooth clustering.Then

max-RGB was used to find the maximum value of each channel to estimate the value of each pixel

construct the initial illumination map

and optimize this map based on local consistency and alternating direction minimization techniques. Adaptive Gamma correction performed non-linear relabeling on the optimized illumination map

providing the final illumination map.The input image could be enhanced by using the information of the final illumination map

and the enhanced image was fused with the detail layer to obtain a clearer and more detailed image.The proposed model exhibited better performance compared with the LE algorithm

GC algorithm

HE algorithm

SSR algorithm

MSR algorithm

MSRCR algorithm

and MSRCP algorithm:the edge intensity is 1.00e + 02

average gradient is 10.520 6

and spatial frequency is 52.050 8. The highest image definition achieved is 14.656 2

which is superior to other algorithms considered in this study

in both subjective and objective evaluations. The experimental results show that the proposed algorithm can generate imageswith higher definition

clearer edges

and richer textures.

关键词

Keywords

references

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