An Image Fusion Technique Based on Hadamard Transform and HVS

R. Vadhi, V. S. Kilari, S. Srinivas Kumar

Abstract


The main endeavor of image fusion is to obtain an image that contains more visual quality information than any one of the source images. In general, the source images may be multi focus, multi modality, multi resolution, multi temporal, panchromatic, satellite images considered for fusion. This paper discusses image fusion using Hadamard Transform (HT). In this work, Human Visual System (HVS) is investigated for image fusion in the HT domain. The proposed fusion process contains three important parts, (1) divide source images into sub images / blocks and transform them into HT domain. (2) multiply transformed coefficients with HVS based weightage matrix of HT and select the highest value from them (3) fuse the corresponding block of selected coefficients from source images in to an empty image. The utility of HVS makes the coefficients more significant. The performance of the proposed method is analyzed and compared with Discrete Wavelet Transform (DWT) based image fusion technique. Implementation in HT domain is simple and time saving when compared with DWT.


Keywords


Hadamard Transform; HT; human visual system; HVS; discrete wavelet transform; DWT; image Fusion

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References


A. Garzelli, “Possibilities and limitations of the use of wavelets in image fusion”, IEEE Geoscience and Remote Sensing Symposium. Vol. 1, pp. 66-68, 2002

G. Paella, “A general frame work for multiresolution image fusion: from pixels to regions”, Information Fusion, Vol. 4, No. 4, pp. 259-280, 2003

D. A. Godse, D. S. Bormane, “Wavelet based image fusion using pixel based maximum selection rule”, International Journal of Engineering Science and Technology , Vol. 3, No. 7, pp. 5572-5577, 2011

N. Mitianoudi, T. Stathaki, “Pixel-based and region-based image fusion schemes using ICA bases”, Information Fusion, Vol. 8, No. 2, pp. 131-142, 2007

J. Jayanth, S. Koliwad, “Performance degraded by the sensor noise at pixel level image fusion”, International Journal of Computer Applications, Vol. 8, No. 9, pp. 23-28, 2010

J. Tang, “A contrast based image fusion technique in the DCT domain”, Digital Signal Processing, Vol. 14, No. 3, pp. 218-226, 2004

J. Johnson, M.Puschel, “In search of the optimal walsh-hadamard transform”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 6, pp. 3347-3350, 2000, pp. 3347-3350, 2000.

J. Mannos, D. Sakrison, “The effect of a visual fidelity criterion in the encoding of images”, IEEE. Trans. Information Theory, Vol. 20, No. 4, pp. 525-536, 1974

T. N. Pappas, J. P. Allebach, D. L. Nehhoff, “Model-based digital halftoning”, IEEE Signal Processing Magazine, Vol. 20, No. 4, pp.14-27, 2003

J. Sullivan, L. Ray, R. Miller, “Design of minimum visual modulation halftone patterns”, IEEE Transactions on Systems, Man and Cybernetics, Vol. 21, No. 1, pp. 33-38, 1991

K. Veeraswamy, S. Srinivaskumar, B. N. Chatterji, “Designing quantization table for Hadamard transform based on human visual system for image compression”, Graphics, Vision and Image Processing, Vol. 7, No. 3, pp. 31-38, 2007

L. Zhang, L. Zhang, X. Mou, D. Zhang, “FSIM: A Feature Similarity Index for Image Quality Assessment”, IEEE transactions on Image Processing, Vol. 20, No. 8, pp.2378-2386, 2011

G. Qu, D. Zhang, P. Yan, “Information measure for performance of image fusion”, Electronic Letters, Vol. 38, No. 7, pp. 313-315, 2002

C. S. Xydeas, V. Petrovic, “Objective image fusion performance measure”, Electronic Letters, Vol. 36, No. 4, pp. 308-309, 2000

S. Daly, “Subroutine for the generation of a two dimensional human visual contrast sensitivity function”, Tech. Rep 2332037, Eastman Kodak, 1987

O. Rockinger, “Image sequence fusions using a shift-invariant wavelet transform”, IEEE International Conference on Image Processing, Vol. 3, pp. 288-291, 1997

M. B. A. Haghighat, A. Aghagolzadeh, H. Seyedarabi, “Multi-focus image fusion for visual sensor networks in DCT domain”, Computers & Electrical Engineering, Vol. 37, No. 5, pp. 789-797, 2011

R. Vadhi, V. Kilari, S. K. Samayamantula, “Uniform based approach for image fusion”, in Eco-friendly Computing and Communication Systems, Springer Berlin Heidelberg, pp 186-194, 2012

D. Srinivas Rao, M. Seetha, M. H. M. Krishna Prasad, “Quality assessment parameters for iterative image fusion using fuzzy and neuro fuzzy logic and applications”, Procedia Technology, Vol. 19, pp. 888-894, 2015

H. Lin, Y. Tian, R. Pu, L. Liang, “Remotely Sensing Image Fusion Based on Wavelet Transform and Human Visual System”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 8, No. 7, pp. 291-298, 2015

Y. Yang, W. Zheng, S. Huang, “Effective Multifocus Image Fusion Based on HVS and BP Neural Network”, The Scientific World Journal, Vol. 2014, Article ID 281073, pp.1-8, 2014




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