采用两阶段混合策略实现的低照度图像增强算法

徐少平; 陈孝国; 李芬; 林珍玉; 陈晓军

doi:10.12263/DZXB.20201018

您当前的位置：

首页 >

文章列表页 >

采用两阶段混合策略实现的低照度图像增强算法

学术论文 | 更新时间：2025-12-08

- 采用两阶段混合策略实现的低照度图像增强算法
- A Low‑Light Image Enhancement Algorithm Using Two‑Stage Hybrid Strategy
- 电子学报 2021年49卷第11期页码：2166-2170
- 作者机构：
  
  南昌大学信息工程学院，江西南昌 330031
- 作者简介：
  
  [ "徐少平(通讯作者)　男，1976年5月出生于江西省九江市. 博士, 南昌大学信息工程学院计算机科学与技术系教授, 博士生导师. 主要研究方向为图形图像处理、机器视觉、虚拟手术仿真等. E‑mail:xushaoping@ncu.edu.cn" ]
  [ "陈孝国　男，1994年生，河南濮阳人. 现为南昌大学硕士研究生, 主要研究方向为图像处理与计算机视觉." ]
- 基金信息：
  
  国家自然科学基金(62162043)
- DOI：10.12263/DZXB.20201018
  中图分类号： TP391.41
- 收稿：2020-09-13，
  
  修回：2021-03-31，
  
  纸质出版：2021-11-25
- 稿件说明：
移动端阅览
徐少平,陈孝国,李芬等.采用两阶段混合策略实现的低照度图像增强算法[J].电子学报,2021,49(11):2166-2170.

XU Shao-ping,CHEN Xiao-guo,LI Fen,et al.A Low‑Light Image Enhancement Algorithm Using Two‑Stage Hybrid Strategy[J].ACTA ELECTRONICA SINICA,2021,49(11):2166-2170.
徐少平,陈孝国,李芬等.采用两阶段混合策略实现的低照度图像增强算法[J].电子学报,2021,49(11):2166-2170. DOI： 10.12263/DZXB.20201018.

XU Shao-ping,CHEN Xiao-guo,LI Fen,et al.A Low‑Light Image Enhancement Algorithm Using Two‑Stage Hybrid Strategy[J].ACTA ELECTRONICA SINICA,2021,49(11):2166-2170. DOI： 10.12263/DZXB.20201018.

摘要

在深入分析现有各主流低照度图像增强(Low Light Image Enhancement

LLIE)算法的基础上，提出了一种采用两阶段混合策略实现的低照度图像增强(Hybrid LLIE

HLLIE)算法.具体地，在第一阶段，对于给定的低照度图像，利用互补效果较好的Fu和Ying两个主流LLIE算法分别对其进行增强预处理，所得到的两张增强后图像称为初步增强图像；在第二阶段，将所得到的两张初步增强图像输入到预先训练好的多通道浅层卷积神经网络(Multi‑channel Shallow Convolution Neural Network

MSCNN)模型中，由MSCNN模型将两张初步增强图像优化组合为一张具有更高图像质量的最终增强图像.实验结果表明：与各主流LLIE算法相比，所提出的HLLIE算法在各个客观图像质量评价指标上有显著优势，人工主观评价亦能证实这一点.

Abstract

After carefully analyzing the characteristics of existing low light image enhancement (LLIE) algorithms

we proposed a hybrid LLIE (HLLIE) algorithm with two-stage hybrid strategy. Specifically

in the first stage

for a given low-light image

we chose the complementary LLIE algorithms

i.e.

Fu and Ying

to enhance the low-light image

respectively. In the second stage

the two preliminary enhanced images were used as the inputs of the multi-channel shallow convolution neural network (MSCNN)

and then the pre-trained MSCNN conducted optimal combination of the two preliminary enhanced images to achieve further enhancement regarding the image quality. The experimental results show that

compared with the state-of-the-art LLIE algorithms

the HLLIE algorithm has obvious advantages in terms of the objective image quality metrics on the low-light images

and the subjective evaluation also can confirm this.

关键词

Keywords

references

Wang Y F , Liu H M , Fu Z W . Low-light image enhancement via the absorption light scattering model [J]. IEEE Transactions on Image Processing , 2019 , 28 ( 11 ): 5679 - 5690 .

Ren Y R , Ying Z Q , Li T H , et al . LECARM: Low-light image enhancement using camera response model [J]. IEEE Transactions on Circuits and Systems for Video Technology , 2019 , 29 ( 4 ): 968 - 981 .

Guo Y H , Ke X , Ma J , et al . A pipeline neural network for low-light image enhancement [J]. IEEE Access , 2019 , 7 : 13737 - 13744 .

Park S , Yu S , Kim M , et al . Dual autoencoder network for Retinex-based low-light image enhancement [J]. IEEE Access , 2018 , 6 : 22084 - 22093 .

Cheng H D , Shi X J . A simple and effective histogram equalization approach to image enhancement [J]. Digital Signal Processing , 2004 , 14 ( 2 ): 158 - 170 .

Fu X Y , Zeng D L , Yue H , et al . A weighted variational model for simultaneous reflectance and illumination estimation [A]. 2016 IEEE Conference on Computer Vision and Pattern Recognition [C]. USA : IEEE , 2016 . 2782 - 2790 .

Ying Z Q , Ge L , Ren Y R , et al . A new image contrast enhancement algorithm using exposure fusion framework [A]. International Conference on Computer Analysis of Images and Patterns [C]. Sweden : Springer Verlag , 2017 . 36 - 46 .

Loh Y P , Liang X F , Chan C S . Low-light image enhancement using Gaussian process for features retrieval [J]. Signal Processing: Image Communication , 2019 , 74 : 175 - 190 .

Guo X J , Yu L , Ling H B . LIME: Low-light image enhancement via illumination map estimation [J]. IEEE Transactions on Image Processing , 2017 , 26 ( 2 ): 982 - 993 .

徐少平 , 刘婷云 , 林珍玉 , 等 . 深度卷积神经网络降噪模型的技术瓶颈与研究展望 [J]. 中国图象图形学报 , 2019 , 24 ( 8 ): 1207 - 1214 .

Xu S P , Liu T Y , Lin Z Y , et al . Main bottlenecks and research prospects of the deep convolutional neural network-based denoising model [J]. Journal of Image and Graphics , 2019 , 24 ( 8 ): 1207 - 1214 . (in Chinese)

Zhang K , Zuo W M , Chen Y J , et al . Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising [J]. IEEE Transactions on Image Processing , 2017 , 26 ( 7 ): 3142 - 3155 .

Cai J R , Gu S H , Zhang L . Learning a deep single image contrast enhancer from multi-exposure images [J]. IEEE Transactions on Image Processing , 2018 , 27 ( 4 ): 2049 - 2062 .

Fu X Y , Liao Y H , Zeng D L , et al . A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation [J]. IEEE Transactions on Image Processing , 2015 , 24 ( 12 ): 4965 - 4977 .

Li M D , Liu J Y , Yang W H , et al . Structure-revealing low-light image enhancement via robust Retinex model [J]. IEEE Transactions on Image Processing , 2018 , 27 ( 6 ): 2828 - 2841 .

Fu X Y , Zeng D L , Yue H , et al . A fusion-based enhancing method for weakly illuminated images [J]. Signal Processing , 2016 , 129 : 82 - 96 .

Wang Z , Li Q . Information content weighting for perceptual image quality assessment [J]. IEEE Transactions on Image Processing , 2011 , 20 ( 5 ): 1185 - 1198 .

Ying Z Q , Ge L , Wen G . A bio-inspired multi-exposure fusion framework for low-light image enhancement [J]. Journal of Latex Class Files , 2015 , 14 ( 8 ): 1 - 10 .

Kai Z , Ma K D , Hassen R , et al . Perceptual evaluation of multi-exposure image fusion algorithms [A]. International Workshop on Quality of Multimedia Experience [C]. Singapore : IEEE , 2014 . 7 - 12 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于任务解耦的低照度图像增强方法

采用深度学习与图像融合混合实现策略的低照度图像增强算法