多尺度权重评估的MSRCR混合曝光成像算法

doi:10.3778/j.issn.1002-8331.2005-0347

计算机工程与应用 ›› 2021, Vol. 57 ›› Issue (17): 224-229.DOI: 10.3778/j.issn.1002-8331.2005-0347

多尺度权重评估的MSRCR混合曝光成像算法

吴卓钊，范科峰，莫玮

1.桂林电子科技大学电子工程与自动化学院，广西桂林 541004
2.中国电子技术标准化研究院，北京 100007

出版日期:2021-09-01 发布日期:2021-08-30

MSRCR Hybrid Fusion Exposure Imaging Algorithm Based on Multi-scale Weight Assessment

WU Zhuozhao, FAN Kefeng, MO Wei

1.School of Electronic Engineering and Automation, Guilin University of Electronic Science and Technology, Guilin, Guangxi 541004, China
2.China Institute of Electronic Technology Standardization, Beijing 100007, China

Online:2021-09-01 Published:2021-08-30

摘要/Abstract

摘要：

针对混合曝光成像算法过程中会出现低曝光处细节丢失且颜色失真饱和度不佳导致视觉观感下降的问题，提出一种多尺度权重评估的MSRCR（Multi-Scale Retinex with Color Restoration，MSRCR）混合曝光融合算法。基于Retinex模型将待融合图像分解为亮度分量与反射光分量，对亮度分量结合ACES函数构造光照补偿归一化函数进行处理，对反射光分量加入颜色恢复函数提升色彩细节；分别从曝光量、饱和度、对比度、色域四个尺度设计图像融合权重值，通过多尺度评估优化融合比例；利用Laplacian金字塔融合算法进行多尺度权重融合获得最终图像。实验结果表明，与传统的图像融合算法相比，该算法处理效果较好，有效降低了暗处失真率，提升了视觉信息保真度。

关键词: 高动态范围, 带色彩恢复的多尺度视网膜增强算法（MSRCR）, 光照补偿, 多尺度评估, 金字塔融合

Abstract:

In order to solve the problem of the loss of details in the low-exposure area and the decrease of visual perception due to the poor color distortion and saturation in the mixed exposure imaging algorithm, a Multi-Scale Retinex with Color Restoration（MSRCR） is proposed based on the multi-scale weight assessment.Firstly, based on the Retinex theory model, the image to be merged is decomposed into brightness component and reflected light component, and the normalized function of illumination compensation is constructed by combining the brightness component and ACES function for processing. The color recovery function is added to the reflected light component to improve the color details.Secondly, the image fusion weight values are designed from the four scales of exposure, saturation, contrast and color gamut, respectively, and the fusion ratio is optimized through multi-scale evaluation.Finally, the Laplacian pyramid fusion algorithm is used for multi-scale weight fusion to obtain the final image.Compared with the traditional image fusion algorithm, the experimental results show that this algorithm has better processing effect, effectively reduces the shadow distortion rate and improves the fidelity of visual information.

Key words: high dynamic range, Multi-Scale Retinex with Color Restoration（MSRCR）, lighting compensation, multi-scale assessment, pyramid fusion

吴卓钊，范科峰，莫玮. 多尺度权重评估的MSRCR混合曝光成像算法[J]. 计算机工程与应用, 2021, 57(17): 224-229.

WU Zhuozhao, FAN Kefeng, MO Wei. MSRCR Hybrid Fusion Exposure Imaging Algorithm Based on Multi-scale Weight Assessment[J]. Computer Engineering and Applications, 2021, 57(17): 224-229.

[1]	朱惠娟，宗平，丛玉华. 基于权重池的多尺度图像质量评估方法[J]. 计算机工程与应用, 2021, 57(3): 215-221.
[2]	付争方1，朱虹2. 多尺度细节融合的多曝光高动态图像重建[J]. 计算机工程与应用, 2018, 54(24): 182-187.
[3]	李林国，李淑敬，张宝玉. 一种分组实现的高动态范围图像显示方法[J]. 计算机工程与应用, 2013, 49(9): 180-182.
[4]	芦碧波，李祎. 基于非局部均值的多尺度色调映射[J]. 计算机工程与应用, 2013, 49(16): 162-164.
[5]	陈丹1，王国胤1，2，龚勋1，2，杨勇1. 一种适用于人脸检测的自适应光照补偿方法[J]. 计算机工程与应用, 2012, 48(22): 175-178.
[6]	焦立男1，孙朝云1，沙爱民2. 非均匀光照图像的子块部分重叠对数变换[J]. 计算机工程与应用, 2011, 47(9): 196-199.
[7]	郑伟华1，2，戴永2. 非线性复合邻域人脸光照补偿[J]. 计算机工程与应用, 2011, 47(13): 195-198.
[8]	冯浩^1，2，陈勇¹，王宪保¹. 结合DCT和WT的多分类器融合的光照人脸识别[J]. 计算机工程与应用, 2010, 46(8): 153-155.
[9]	焦竹青，徐保国. HSV变换和同态滤波的彩色图像光照补偿[J]. 计算机工程与应用, 2010, 46(30): 142-144.
[10]	华顺刚¹,张静²,刘婷¹. 高动态范围图像及其色彩迁移[J]. 计算机工程与应用, 2008, 44(8): 63-66.
[11]	刘丽华. 基于线性子空间和商图像理论的人脸光照补偿[J]. 计算机工程与应用, 2008, 44(25): 189-192.
[12]	王家亮,顾耀林 . 基于JPEG2000的高动态范围图象压缩[J]. 计算机工程与应用, 2007, 43(26): 81-83.
[13]	聂祥飞^1,2,郭军². 人脸识别中基于多小波变换的光照补偿[J]. 计算机工程与应用, 2007, 43(18): 1-3.

多尺度权重评估的MSRCR混合曝光成像算法

MSRCR Hybrid Fusion Exposure Imaging Algorithm Based on Multi-scale Weight Assessment

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 13

编辑推荐

Metrics