Multi-focus image fusion based on sparse representation with adaptive sparse domain selection

Proceedings - 2013 7th International Conference on Image and Graphics, ICIG 2013

Volumn , Issue , 2013, Pages 591-596

  Liu, Yu ^a   Wang, Zengfu ^a,b  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM STABILITY; GENERAL OPTIMIZATIONS; GRADIENT INFORMATIONS; IMAGE PROCESSING APPLICATIONS; MULTIFOCUS IMAGE FUSION; OBJECTIVE EVALUATION CRITERIA; REDUNDANT DICTIONARIES; SPARSE REPRESENTATION;

IMAGE PROCESSING;

IMAGE FUSION;

EID: 84891313062     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIG.2013.123     Document Type: Conference Paper

References (15)

1. G. Piella. A general framework for multiresolution imagefusion: from pixels to regions. Information Fusion, 4(4): 259-280, 2003. 1
2. M. Elad and M. Aharon. Image denoising via sparse andredundant representations over learned dictionaries. IEEETrans. on Image Processing, 15(2):3736-3745, 2006. 1, 3, 5
3. J. Yang, J. Wright, T. Huang and Y. Ma. Imagesuper-resolution via sparse representation. IEEE Trans. onImage Processing, 19(11): 2861-2873. 2010. 1
4. W. Dong, L. Zhang, G. Shi, and X. Wu. Image deblurringand super-resolution by adaptive sparse domain selection andadaptive regularization. IEEE Trans. on Image Processing, 20(7):1838-1857, 2011. 1, 2
5. B. Yang and S. Li. Multifocus image fusion and restorationwith sparse representation. IEEE Trans. on Instrumentationand Measurement, 59 (4):884-892, 2010. 1, 2, 3, 4, 5, 6
6. B. Yang and S. Li. Pixel-level image fusion withsimultaneous orthogonal matching pursuit. InformationFusion, 13(1):10-19, 2012. 1, 2
7. N. Yu, T. Qiu, F. Bi and A. Wang. Image features extractionand fusion based on joint sparse representation. IEEE Journalof Selected Topics in Signal Processing, 5(5):1074-1082, 2011. 1, 2
8. G. Yang, X. Xu and H. Man. Optimum image fusion viasparse representation. 20th Annual Wireless and OpticalCommunications Conference, 1-4, 2011. 1, 2
9. M. Aharon, M. Elad and A. Bruckstein. K-SVD: analgorithm for designing overcomplete dictionaries for sparserepresentation. IEEE Transactions on Signal Processing, 54(11):4311-4322, 2006. 2
10. D. G. Lowe. Distinctive image features from scale-invariantkeypoints. International Journal of Computer Vision,60(2):91-110, 2004. 3
11. X. Qu, J. Yan, H Xiao and Z. Zhu. Image fusion algorithmbased on spatial frequency-motivated plus coupled neuralnetwork in nonsubsampled contourlet transform domain. Acta Automatica Sinica, 34(12):1508-1514, 2008. 4
12. Investigations of Image Fusion. Electrical Engineering andComputer Science Department, Lehigh University. http://www. ece. lehigh. edu/SPCRL/IF/image-fusion. htm. 4
13. G. Piella and H. Heijmans. A new quality metric for imagefusion. In Proceedings of the IEEE International Conferenceon Image Processing, 173-176, 2003. 4
14. C. S. Xydeas and V. Petrovic. Objective image fusionperformance measure. Electronics Letters 36(4):308-309, 2000. 4
15. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: From error measurement tostructural similarity. IEEE Transactions on Image Processing,3(4):600-612, 2004. 4