双分支金字塔网络的微光图像增强算法

陈清江; 顾媛; 李金阳

doi:10.37188/CJLCD.2021-0221

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双分支金字塔网络的微光图像增强算法

图像处理 | 更新时间：2022-03-25

- 双分支金字塔网络的微光图像增强算法
- Low-light level image enhancement algorithm based on double-branch pyramid model
- 液晶与显示 2022年37卷第3期页码：395-404
- 作者机构：
  
  西安建筑科技大学理学院，陕西西安 710055
- 作者简介：
  
  [ "陈清江 (1966-)，男，河南信阳人，博士，教授，2006年于西安交通大学获博士学位，主要从事小波分析、图像处理与信号处理方面的研究。E-mail：qjchen66xytu@126.com" ]
  [ "顾媛 (1998-)，女，陕西汉中人，硕士研究生，2020年于咸阳师范学院获得学士学位，主要从事小波分析、图像处理与信号处理方面的研究。E-mail：2544020739@qq.com" ]
  [ "李金阳 (1996-)，男，陕西商洛人，硕士研究生，2019年于咸阳师范学院获得学士学位，主要从事小波分析，图像处理与信号处理方面的研究。E-mail：1057042448@qq.com" ]
- 基金信息：
  
  国家自然科学基金(61902304)
- DOI：10.37188/CJLCD.2021-0221
  中图分类号： TP391.4
- 收稿日期：2021-08-25，
  
  修回日期：2021-10-04，
  
  纸质出版日期：2022-03
- 稿件说明：
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陈清江, 顾媛, 李金阳. 双分支金字塔网络的微光图像增强算法[J]. 液晶与显示, 2022,37(3):395-404.

Qing-jiang CHEN, Yuan GU, Jin-yang LI. Low-light level image enhancement algorithm based on double-branch pyramid model[J]. Chinese journal of liquid crystals and displays, 2022, 37(3): 395-404.
陈清江, 顾媛, 李金阳. 双分支金字塔网络的微光图像增强算法[J]. 液晶与显示, 2022,37(3):395-404. DOI： 10.37188/CJLCD.2021-0221.

Qing-jiang CHEN, Yuan GU, Jin-yang LI. Low-light level image enhancement algorithm based on double-branch pyramid model[J]. Chinese journal of liquid crystals and displays, 2022, 37(3): 395-404. DOI： 10.37188/CJLCD.2021-0221.

摘要

针对微光图像存在亮度低、细节丢失严重以及现有算法增强后的微光图像存在曝光过度、颜色失真等问题，提出双分支金字塔网络的微光图像增强算法。首先，将微光图像从RGB颜色空间转换到HSV颜色空间。其次，对

分量采用双分支金字塔网络的微光图像增强算法，自适应获取图像特征。网络结构由并行双分支构成，含层级残差模块的分支有效增强

分量亮度，含特征金字塔注意力模块的分支获取深层特征信息。最后，对双分支结构提取的信息进行融合，并将增强后的图像从HSV颜色空间转换回RGB颜色空间。所提算法在真实图像上的峰值信噪比及结构相似度的均值分别达到29.451 dB和0.930 1，均高于其他对比算法。实验结果表明，所提算法对

分量进行增强提高了图像亮度，有效地恢复了图像细节。

Abstract

A low light level image enhancement algorithm based on double branch pyramid model is proposed. It is used to solve the problems of low brightness

serious loss of detail

over exposure and color distortion of the low light level image enhanced by existing algorithms. Firstly

the low-light level image is converted from RGB color space to HSV color space. Secondly

the low-light level image enhancement network of double branching pyramid network is constructed for the

component

and the image features are obtained adaptively. The network structure is composed of two parallel branches. The branch with hierarchical residual module can effectively enhance the brightness of

component

and the branch with feature pyramid attention module can obtain deep feature information. Finally

the information extracted from the dual-branch structure is fused

and the enhanced image is converted from HSV color space to RGB color space. The peak signal-to-noise ratio and the average structure similarity of the proposed algorithm on real images are 29.451 dB and 0.930 1 respectively

which are higher than other comparison algorithms. Experimental results show that the enhancement of the

component can effectively improve the image brightness and restore the image details.

关键词

Keywords

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