Feature-Based Image Fusion Quality Metrics

Image fusion quality metrics have evolved from image processing quality metrics. They measure the quality of fused images by estimating how much localized information has been transferred from the source images into the fused image. However, this technique assumes that it is actually possible to fuse two images into one without any loss. In practice, some features must be sacrificed and relaxed in both source images. Relaxed features might be very important, like edges, gradients and texture elements. The importance of a certain feature is application dependant. This paper presents a new method for image fusion quality assessment. It depends on estimating how much valuable information has not been transferred.

[1]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[2]  Rick S. Blum,et al.  On estimating the quality of noisy images , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

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

[4]  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).

[5]  Tom E. Bishop,et al.  Blind Image Restoration Using a Block-Stationary Signal Model , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

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

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

[8]  Lucien Wald,et al.  Data fusion : a conceptual approach for an efficient exploitation of remote sensing images , 1998 .

[9]  Lucien Wald,et al.  Some terms of reference in data fusion , 1999, IEEE Trans. Geosci. Remote. Sens..

[10]  S. Nahavandi,et al.  A Quadtree Driven Image Fusion Quality Assessment , 2007, 2007 5th IEEE International Conference on Industrial Informatics.

[11]  Yun He,et al.  A multiscale approach to pixel-level image fusion , 2005, Integr. Comput. Aided Eng..

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

[13]  Timo Rolf Bretschneider,et al.  Objective content-dependent quality measures for image fusion of optical data , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[14]  Nishan Canagarajah,et al.  A Similarity Metric for Assessment of Image Fusion Algorithms , 2008 .

[15]  Xin Liu,et al.  A novel similarity based quality metric for image fusion , 2008, Inf. Fusion.

[16]  Zheng Liu,et al.  Image Fusion Algorithm Assessment Based on Feature Measurement , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[17]  R.S. Blum,et al.  Experimental tests of image fusion for night vision , 2005, 2005 7th International Conference on Information Fusion.

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

[19]  Zhou Wang,et al.  Why is image quality assessment so difficult? , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[20]  Timo Rolf Bretschneider,et al.  A fusion evaluation approach with region relating objective function for multispectral image sharpening , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..