Multi-exposure image fusion algorithm based on Retinex theory

doi:10.11772/j.issn.1001-9081.2018112382

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (7): 2087-2092.DOI: 10.11772/j.issn.1001-9081.2018112382

• Virtual reality and multimedia computing • Previous Articles Next Articles

Multi-exposure image fusion algorithm based on Retinex theory

WAGN Keqiang, ZHANG Yushuai, WANG Baoqun

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2018-12-03 Revised:2019-01-21 Online:2019-07-10 Published:2019-07-15
Supported by:
This work is partially supported by the Program for Changjiang Scholars and Innovative Research Team in University (IRT_16R72).

基于Retinex理论的多曝光图像融合算法

王克强, 张雨帅, 王保群

重庆邮电大学通信与信息工程学院, 重庆 400065

通讯作者: 张雨帅
作者简介:王克强(1976-),男,山东青岛人,正高级工程师,硕士,主要研究方向:移动终端产品研发;张雨帅(1994-),男,山东济宁人,硕士研究生,主要研究方向:低照度图像增强、多曝光图像融合;王保群(1994-),男,山东聊城人,硕士研究生,主要研究方向:机器学习、数据挖掘。
基金资助:
长江学者和创新团队发展计划项目（IRT_16R72）。

Abstract

Abstract:

Multi-exposure image fusion technology directly combines a sequence of images with the same scene but different exposure levels into a high-quality image with more details of scene. Aiming at the problems of poor local contrast difference and color distortion of existing algorithms, a new multi-exposure image fusion algorithm was proposed based on Retinex theoretical model. Firstly, based on Retinex theoretical model, the exposure sequence images were divided into an illumination component sequence and a reflection component sequence by using the illumination estimation algorithm, and then two sets of sequences were processed by different fusion methods. For the illumination component, the variation characteristics of global brightness of scene were guaranteed and the effects of overexposed and underexposed regions were weakened, while for the reflection component, the evaluation parameters of moderate exposure were used to better preserve the color and detail information of scene. The proposed algorithm was analyzed from both subjective and objective aspects. The experimental results show that compared with traditional algorithm based on image domain synthesis, the proposed algorithm has an average increase of 1.7% in Structural SIMilarity (SSIM) and has better effect in the processing of image color and local details.

Key words: high dynamic range imaging, multi-exposure image, image fusion, Retinex, well-exposedness

摘要：

多曝光图像融合技术是将一组场景相同但曝光程度不同的图像序列直接融合成为一幅含有更多场景细节信息的高质量图像。针对现有算法局部对比度差和色彩失真的问题，结合Retinex理论模型提出了一种新的多曝光图像融合算法。首先，基于Retinex理论模型，利用光照估计算法将曝光序列图像分为入射光分量序列和反射光分量序列，然后分别采用不同的融合方法对这两组序列进行处理。对于入射光分量，要保证场景的全局亮度的变化特性并且削弱过曝光和欠曝光区域的影响；而对于反射光分量，要采用适度曝光的评价参数来更好地保留场景的色彩及细节信息。分别从主观和客观两方面对所提算法进行了分析。实验结果表明，同传统基于图像域合成的算法相比，该算法在结构相似度（SSIM）上平均提升了1.7%，另外在图像色彩和局部细节上的处理效果更好。

关键词: 高动态范围成像, 多曝光图像, 图像融合, Retinex, 曝光适度

CLC Number:

TP391.413

WAGN Keqiang, ZHANG Yushuai, WANG Baoqun. Multi-exposure image fusion algorithm based on Retinex theory[J]. Journal of Computer Applications, 2019, 39(7): 2087-2092.

王克强, 张雨帅, 王保群. 基于Retinex理论的多曝光图像融合算法[J]. 计算机应用, 2019, 39(7): 2087-2092.

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Multi-exposure image fusion algorithm based on Retinex theory

基于Retinex理论的多曝光图像融合算法

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