基于自适应分割的多曝光图像融合算法

doi:10.11772/j.issn.1001-9081.2019061114

计算机应用 ›› 2020, Vol. 40 ›› Issue (1): 252-257.DOI: 10.11772/j.issn.1001-9081.2019061114

• 虚拟现实与多媒体计算 • 上一篇下一篇

基于自适应分割的多曝光图像融合算法

王书朋, 赵瑶

西安科技大学通信与信息工程学院, 西安 710054

收稿日期:2019-06-27 修回日期:2019-09-04 出版日期:2020-01-10 发布日期:2019-10-08
作者简介:王书朋(1975-),男,江苏常熟人,副教授,博士,主要研究方向:图像处理、模式识别、计算机视觉;赵瑶(1995-),女,陕西西安人,硕士研究生,主要研究方向:多曝光图像融合、模式识别。

Multi-exposure image fusion algorithm based on adaptive segmentation

WANG Shupeng, ZHAO Yao

College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China

Received:2019-06-27 Revised:2019-09-04 Online:2020-01-10 Published:2019-10-08
Contact: 王书朋

摘要/Abstract

摘要： 针对传统多曝光图像融合存在颜色和细节信息保留不完整的问题，提出了一种新的基于自适应分割的多曝光图像融合算法。首先，采用超像素分割将输入图像分割为颜色一致的图像块，再利用结构分解将图像块分解为三个独立分量。根据各分量特点设计不同融合规则，以保留源图像中的颜色和细节信息。然后，采用引导滤波平滑各分量的权重图以及信号强度分量和亮度分量，有效地克服块效应缺陷，保留源图像中的边缘信息，减少伪影。最后，重构融合后的三个分量，得到最终的融合图像。实验结果表明，与传统的融合算法相比，所提算法在互信息（MI）上平均提升了53.6%、标准差（SD）上平均提升了24.0%。该算法能够有效地保留输入图像的颜色和细节纹理信息。

关键词: 多曝光图像, 图像融合, 超像素分割, 融合规则, 引导滤波

Abstract: Aiming at the insufficient preservation of color and details existed in traditional multi-exposure image fusion, a novel multi-exposure image fusion algorithm based on adaptive segmentation was proposed. Firstly, the input image was divided into blocks with the same color by super-pixel segmentation. Then structural decomposition was conducted on the image blocks to obtain three individual components. Different fusion rules were designed according to the characteristics of each component, so as to preserve the color and details in original images. Then, the weight map of each component, signal strength component and brightness component were smoothed by guided filtering, effectively overcoming the problem of block effect, retaining the edge information in the source image and reducing the artifacts. Finally, the fusion image was obtained by reconstructing three fused components. The experimental results show that, compared to the traditional fusion algorithms, the proposed algorithm has the average increase of 53.6% in Mutual Information (MI) and 24.0% in Standard Deviation (SD) respectively. The proposed image fusion algorithm can effectively preserve the color and texture details of input images.

Key words: multi-exposure image, image fusion, super-pixel segmentation, fusion rule, guided filtering

中图分类号:

TP391.413

王书朋, 赵瑶. 基于自适应分割的多曝光图像融合算法[J]. 计算机应用, 2020, 40(1): 252-257.

WANG Shupeng, ZHAO Yao. Multi-exposure image fusion algorithm based on adaptive segmentation[J]. Journal of Computer Applications, 2020, 40(1): 252-257.

参考文献

[1] 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.(WANG K Q, ZHANG Y S, WANG B Q. Multi-exposure image fusion algorithm based on Retinex theory[J]. Journal of Computer Applications, 2019, 39(7):2087-2092.
[2] 李卫中,易本顺,邱康,等.细节保留的多曝光图像融合[J].光学精密工程,2016,24(9):2283-2292.(LI W Z, YI B S, QIU K, et al. Detail preserving reserving multi-exposure imaging fusion[J]. Optics and Precision Engineering, 2016, 24(9):2283-2292.)
[3] LI Y, SUN Y, ZHENG M, et al. A novel multi-exposure image fusion method based on adaptive patch structure[J]. Entropy, 2018, 20(12):935.
[4] MERTENS T, KAUTZ J, REETH F V. Exposure fusion[C]//Proceedings of the 15th Pacific Conference on Computer Graphics and Applications. Piscataway:IEEE, 2007:382-390.
[5] MALIK M H, GILANI S A M, HAQ A. Wavelet based exposure fusion[C/OL]//Proceedings of the 2008 World Congress on Engineering. 2008:688-693[2019-04-20]. http://www.iaeng.org/publication/WCE2008/WCE2008_pp688-693.pdf.
[6] LI S, KANG X, HU J. Image fusion with guided filtering[J]. IEEE Transactions on Image Processing, 2013, 22(7):2864-2875.
[7] SONG M, TAO D, CHEN C, et al. Probabilistic exposure fusion[J]. IEEE Transactions on Image Processing, 2012, 21(1):341-357.
[8] GU B, LI W, WONG J, et al. Gradient field multi-exposure images fusion for high dynamic range image visualization[J]. Journal of Visual Communication and Image Representation, 2012, 23(4):604-610.
[9] LI S, KANG X. Fast multi-exposure image fusion with median filter and recursive filter[J]. IEEE Transactions on Consumer Electronics, 2012, 58(2):626-632.
[10] 付争方,朱虹,薛杉,等.基于Sigmoid函数拟合的多曝光图像直接融合算法[J].仪器仪表学报,2015,36(10):2321-2329.(FU Z F, ZHU H, XUE S, et al. Direct fusion algorithm for multi-exposed images based on sigmoid function fitting[J]. Chinese Journal of Scientific Instrument, 2015, 36(10):2321-2329.)
[11] GOSHTASBY A A. Fusion of multi-exposure images[J]. Image and Vision Computing, 2005, 23(6):611-618.
[12] ZHANG W, HU S, LIU K. Patch-based correlation for deghosting in exposure fusion[J]. Information Sciences, 2017, 415/416:19-27.
[13] MA K, LI H, YONG H, et al. Robust multi-exposure image fusion:a structural patch decomposition approach[J]. IEEE Transactions on Image Processing, 2017, 26(5):2519-2532.
[14] HUANG F, ZHOU D, NIE R, et al. A color multi-exposure image fusion approach using structural patch decomposition[J]. IEEE Access, 2018, 6:42877-42885.
[15] HE K, SUN J, TANG X. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 35(6):1397-1409.
[16] 王春瑶,陈俊周,李炜.超像素分割算法研究综述[J].计算机应用研究,2014,31(1):6-12.(WANG C Y, CHEN J Z, LI W. Review on superpixel segmentation algorithms[J]. Application Research of Computers, 2014, 31(1):6-12.)
[17] ACHANTA R, SHAJI A, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11):2274-2282.
[18] 章星晨,孙刘杰.基于引导滤波和变差函数的图像去雾算法[J].计算机技术与发展,2019,29(6):23-26.(ZHANG X C, SUN L J. Image dehazing algorithm based on guided filtering and variation function[J]. Computer Technology and Development, 2019, 29(6):23-26.)
[19] SHEN J, ZHAO Y, YAN S, et al. Exposure fusion using boosting Laplacian pyramid[J]. IEEE Transactions on Cybernetics, 2014, 44(9):1579-1590.
[20] LIU Y, WANG Z. Dense SIFT for ghost-free multi-exposure fusion[J]. Journal of Visual Communication and Image Representation, 2015, 31:208-224.
[21] VONIKAKIS V, BOUZOS O, ANDREADIS I. Multi-exposure image fusion based on illumination estimation[C]//Proceedings the 2011 IASTED International Conference on Signal and Image Processing and Applications. Crete, Greece:ACTA Press, 2011:135-142.
[22] MERTENS T, KAUTZ J, VAN REETH F. Exposure fusion:a simple and practical alternative to high dynamic range photography[J]. Computer Graphics Forum, 2009, 28(1):161-171.
[23] QU G, ZHANG D, YAN P. Information measure for performance of image fusion[J]. Electronics Letters, 2002, 38(7):313-315.
[24] LIU Y, LIU S, WANG Z. A general framework for image fusion based on multi-scale transform and sparse representation[J]. Information Fusion, 2015, 24:147-164.
[25] 谢伟,王莉明,胡欢君,等.结合引导滤波的自适应多曝光图像融合[J].计算机工程与应用,2019,55(4):193-199.(XIE W, WANG L M, HU H J, et al. Adaptive multi-exposure image fusion with guided filtering[J]. Computer Engineering and Applications, 2019, 55(4):193-199.)
[26] WANG J, WANG W, LI B, et al. Exposure fusion via sparse representation and shiftable complex directional pyramid transform[J]. Multimedia Tools and Applications, 2017, 76(14):15755-15775.

基于自适应分割的多曝光图像融合算法

Multi-exposure image fusion algorithm based on adaptive segmentation

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	张林发, 张榆锋, 王琨, 李支尧. 基于直觉模糊集和亮度增强的医学图像融合[J]. 计算机应用, 2021, 41(7): 2082-2091.
[2]	牛康力, 谌雨章, 沈君凤, 曾张帆, 潘永才, 王绎冲. 基于深度学习的双通道夜视图像复原方法[J]. 计算机应用, 2021, 41(6): 1775-1784.
[3]	吴蕾, 杨晓敏. 改进的基于通道注意力反馈网络的遥感图像融合算法[J]. 计算机应用, 2021, 41(4): 1172-1178.
[4]	黄思远, 张敏情, 柯彦, 毕新亮. 基于显著性检测的图像隐写分析方法[J]. 计算机应用, 2021, 41(2): 441-448.
[5]	成亚玲, 柏智, 谭爱平. 基于引导滤波和差分图像的多聚焦图像融合方法[J]. 计算机应用, 2021, 41(1): 220-224.
[6]	李卫中. 基于场景局部特征的多曝光图像融合[J]. 计算机应用, 2020, 40(8): 2365-2371.
[7]	王瑞尧, 岳雪亭, 周志青, 耿则勋. 最大差值图决策的低照度图像自适应增强算法[J]. 计算机应用, 2020, 40(4): 1164-1170.
[8]	王继霄, 李阳, 王家宝, 苗壮, 张洋硕. 基于SqueezeNet的轻量级图像融合方法[J]. 计算机应用, 2020, 40(3): 837-841.
[9]	牟琦, 唐洋, 李占利, 李洪安. 基于网格运动统计算法和最佳缝合线的密集重复结构图像快速拼接方法[J]. 计算机应用, 2020, 40(1): 239-244.
[10]	刘颖, 王凤伟, 刘卫华, 艾达, 李芸, 杨凡超. 基于亮度分区模糊融合的高动态范围成像算法[J]. 计算机应用, 2020, 40(1): 233-238.
[11]	张斌, 罗晓清, 张战成. 基于TV-L¹结构纹理分解的图像融合质量评价算法[J]. 计算机应用, 2019, 39(9): 2701-2706.
[12]	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.
[13]	吴宗骏, 吴炜, 杨晓敏, 刘凯, Gwanggil Jeon, 袁皓. 改进的基于稀疏表示的全色锐化算法[J]. 计算机应用, 2019, 39(2): 540-545.
[14]	刘斌, 辛迦楠, 谌文江, 肖惠勇. 不可分拉普拉斯金字塔构造及其在多光谱图像融合中的应用[J]. 计算机应用, 2019, 39(2): 564-570.
[15]	高媛, 吴帆, 秦品乐, 王丽芳. 生成对抗残差网络的医学图像融合算法[J]. 计算机应用, 2019, 39(12): 3528-3534.