Objective Quality Assessment for Multiexposure Multifocus Image Fusion

There has been a growing interest in image fusion technologies, but how to objectively evaluate the quality of fused images has not been fully understood. Here, we propose a method for objective quality assessment of multiexposure multifocus image fusion based on the evaluation of three key factors of fused image quality: 1) contrast preservation; 2) sharpness; and 3) structure preservation. Subjective experiments are conducted to create an image fusion database, based on which, performance evaluation shows that the proposed fusion quality index correlates well with subjective scores, and gives a significant improvement over the existing fusion quality measures.

[1]  Rick S. Blum,et al.  A new automated quality assessment algorithm for image fusion , 2009, Image Vis. Comput..

[2]  Shutao Li,et al.  Multifocus Image Fusion and Restoration With Sparse Representation , 2010, IEEE Transactions on Instrumentation and Measurement.

[3]  Peng-wei Wang,et al.  A novel image fusion metric based on multi-scale analysis , 2008, 2008 9th International Conference on Signal Processing.

[4]  Xiuqing Wu,et al.  A novel similarity based quality metric for image fusion , 2008, 2008 International Conference on Audio, Language and Image Processing.

[5]  Stephen T. C. Wong,et al.  Multimodal image fusion for noninvasive epilepsy surgery planning , 1996, IEEE Computer Graphics and Applications.

[6]  Vladimir Petrovic,et al.  Objective image fusion performance measure , 2000 .

[7]  L. Thurstone A law of comparative judgment. , 1994 .

[8]  Peter J. Burt,et al.  Enhanced image capture through fusion , 1993, 1993 (4th) International Conference on Computer Vision.

[9]  Zhou Wang,et al.  Image Sharpness Assessment Based on Local Phase Coherence , 2013, IEEE Transactions on Image Processing.

[10]  Vladimir S. Petrovic,et al.  Subjective tests for image fusion evaluation and objective metric validation , 2007, Inf. Fusion.

[11]  Pramod K. Varshney,et al.  A human perception inspired quality metric for image fusion based on regional information , 2007, Inf. Fusion.

[12]  G. Qu,et al.  Information measure for performance of image fusion , 2002 .

[13]  Joachim M. Buhmann,et al.  Empirical evaluation of dissimilarity measures for color and texture , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[14]  Damon M. Chandler,et al.  ${\bf S}_{3}$: A Spectral and Spatial Measure of Local Perceived Sharpness in Natural Images , 2012, IEEE Transactions on Image Processing.

[15]  Lina J. Karam,et al.  A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB) , 2009, IEEE Transactions on Image Processing.

[16]  Rick S. Blum,et al.  An Overview of Image Fusion , 2005 .

[17]  Alan Chalmers,et al.  Evaluation of tone mapping operators using a High Dynamic Range display , 2005, ACM Trans. Graph..

[18]  Henk J. A. M. Heijmans,et al.  A new quality metric for image fusion , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[19]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[20]  Alan C. Bovik,et al.  A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms , 2006, IEEE Transactions on Image Processing.

[21]  Zhou Wang,et al.  Embedded foveation image coding , 2001, IEEE Trans. Image Process..

[22]  B. S. Manjunath,et al.  Multisensor Image Fusion Using the Wavelet Transform , 1995, CVGIP Graph. Model. Image Process..

[23]  Jianbo Shi,et al.  Generalized Random Walks for Fusion of Multi-Exposure Images , 2011, IEEE Transactions on Image Processing.

[24]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[25]  Oliver Rockinger,et al.  Image sequence fusion using a shift-invariant wavelet transform , 1997, Proceedings of International Conference on Image Processing.

[26]  Z. L. Budrikis,et al.  Picture Quality Prediction Based on a Visual Model , 1982, IEEE Trans. Commun..

[27]  Zhou Wang,et al.  Multifocus Image Fusion Using Local Phase Coherence Measurement , 2009, ICIAR.

[28]  Robert R. Sokal,et al.  A statistical method for evaluating systematic relationships , 1958 .

[29]  A. Ardeshir Goshtasby,et al.  Fusion of multi-exposure images , 2005, Image Vis. Comput..

[30]  Altan Mesut,et al.  A comparative analysis of image fusion methods , 2012, 2012 20th Signal Processing and Communications Applications Conference (SIU).

[31]  R. Luce,et al.  Thurstone and Sensory Scaling : Then and Now , 2004 .

[32]  Alexander Toet,et al.  Perceptual evaluation of different image fusion schemes , 2003 .

[33]  Gemma Piella,et al.  A general framework for multiresolution image fusion: from pixels to regions , 2003, Inf. Fusion.

[34]  Ivor W. Tsang,et al.  Fusing images with different focuses using support vector machines , 2004, IEEE Transactions on Neural Networks.

[35]  Rick S. Blum,et al.  A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application , 1999, Proc. IEEE.