Image Fusion Quality Assessment Method Based on Phase Congruency and Structural Similarity

doi:10.3969/j.issn.1000-1093.2013.12.005

Abstract

Abstract: Structure contour is very important to the human visual system in the quality evaluation of infrared (IR) and visible CCD fusion images. The phase congruency (PC) image, produced by the PC model, can preserve the structure contour feature of image very well, even if the image brightness and contrast have changed. Therefore, an assessment method based on PC is proposed for these considerations. Firstly, two images, the structural similarity (SSIM) images of the fused image relative to the IR and visible CCD images, are produced by the SSIM model, respectively. Then, two PC images of the IR and visible CCD images are produced by the PC model. Afterwards, the SSIM images are weighted by their corresponding PC images, considering the different visual sensitive levels of two different regions, contour region and residual region. At last, the information-entropy coefficients of the IR and visible CCD images are used as the weights to predict image quality assessment. The subjective and objective indexes of experimental images are extracted, and the nonlinear correlation coefficients and Spearman’s rank-order coefficients (SROCCs) are calculated for the first time. Finally the experimental data are statistically analyzed. The results indicate that the proposed method can present more subjective evaluation results more than the previous evaluation methods.

Key words: information processing, image fusion, structural similarity, phase congruency, image quality assessment, consistency between subjective as and objective assessments

CLC Number:

TP391.4

ZHANG Yu-zhen， SUN Jia-song， CHEN Qian, GU Guo-hua, FENG Shi-jie，. Image Fusion Quality Assessment Method Based on Phase Congruency and Structural Similarity[J]. Acta Armamentarii, 2013, 34(12): 1514-1520.

References

［1］ Toet A, Franken E M. Perceptual evaluation of different image fusion schemes[J]. Displays, 2003, 24(1): 25-37.
[2] PetroviAc＇G2 V. Subjective tests for image fusion evaluation and objective metric validation[J]. Information Fusion, 2007, 8(2): 208-216.
[3] 崔岩梅, 倪国强, 钟堰利，等. 利用统计特性进行图像融合效果分析及评价[J]. 北京理工大学学报，2000，20(1): 102-106.
CUI Yan-mei，NI Guo-qiang，ZHONG Yan-li，et al. Analysis and evaluation of the effect of image fusion using statistics parameters[J] . Journal of Beijing Institute of Technology，2000, 20(1) : 102-106. (in Chinese)
[4] 胡许明, 张登福, 南栋, 等. 基于人眼视觉特性的图像视觉质量评价方法[J]. 计算机应用, 2012, 32(7)：1882-1884.
HU Xu-ming，ZHANG Deng-fu，NAN Dong，et al．Method of image visual quality evaluation based on human visual characteristics[J]．Journal of Computer Applications，2012，32(7)：1882-1884. (in Chinese)
[5] 蒋铭, 马兆丰, 杨义先, 等. 基于DWT和视觉加权图像质量评价方法研究[J]. 通信学报, 2011, 32(9): 129-136.
JIANG Ming, MA Zhao-feng, YANG Yi-xian, et al. Image quality evaluation method base on digital wavelet transformed vision weighted[J]. Journal on Communications, 2011,32(9): 129-136. (in Chinese)
[6] Howell C, Moore R, Burks S, et al. An evaluation of fusion algorithms using image fusion metrics and human identification performance[C]∥Defense and Security Symposium. Orlando：International Society for Optics and Photonics，2007：65430V-65430V-11.
[7] 狄红卫, 刘显峰. 基于结构相似度的图像融合质量评价[J]. 光子学报, 2006, 35(5): 766-771.
DI Hong-wei，LIU Xian-feng. Image fusion quality assessment based on structural similarity[J]. Acta Photonica Sinica，2006，35(5): 766-771. (in Chinese)
[8] Wang B, Wang Z, Liao Y, et al. HVS-based structural similarity for image quality assessment[C]∥ The 9th International Conference on Signal Processing. Beijing：IEEE Conference Publication，2008: 1194-1197.
[9] 叶盛楠，苏开娜，肖创柏，等. 基于结构信息提取的图像质量评价[J]. 电子学报，2008，36(5)：856-861.
YE Sheng-nan，SU Kai-na，XIAO Chuang-bai，et al. Image quality assessment based on structural information extraction[J]. Acta Electronica Sinica，2008，36(5)：856-861. (in Chinese)
[10] Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment：from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
[11] 田浩南, 李素梅. 基于边缘的SSIM图像质量客观评价方法[J]. 光子学报，2013，42(1)：110-114.
TIAN Hao-nan，LI Su-mei. Objective evaluation method for image quality based on edge structure similarity[J]. Acta Photonica Sinica，2013，42(1)：110-114. (in Chinese)
[12] 张勇, 金伟其. 基于结构相似度与感兴趣区域的图像融合评价方法[J]. 光子学报，2011，40(2)：311-315.
ZHANG Yong，JIN Wei-qi. Image fusion assessment method based on structural similarity and region of interest[J]. Acta Photonica Sinica，2011，40(2)：311-315. (in Chinese)
[13] Morrone M C，Owens R A. Feature detection from local energy[J]. Pattern Recognition Letters，1987，6(5)：303-313.
[14] Kovesi P. Image features from phase congruency[J]. Journal of Computer Vision Research, 1999, 1(3): 1-26.
[15] Li C, Bovik A C, Wu X. Blind image quality assessment using a general regression neural network[J]. IEEE Transactions on Neural Networks，2011，22(5)：793-799.
[16] 高新波，路文. 视觉信息质量评价方法[M]. 西安:西安电子科技大学出版社，2011：185-187.
GAO Xin-bo，LU Wen. Quality assessment methods for visual information[M]. Xi′an：Xidian University Press，2011：185-187.（in Chinese）

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