Fusion of multifocus noisy images using contourlet transform

Image fusion is one of the important application areas of image processing. It is used to obtain a single composite image with complementary features of various types of images. Presence of noise degrades the quality of an image and its subsequent fusion results, if it is not handled properly. Therefore, in this paper we have proposed an algorithm that combines denoising with fusion process. The proposed algorithm uses level dependent threshold that changes according to the characteristics of coefficients. The proposed method is compared with stationary wavelet transform and dual tree complex wavelet transform based fusion methods. The visual results show that the proposed algorithm gives better results than other methods. The performance of the algorithm is also tested using four quality metrics (peak signal to noise ratio, entropy, standard deviation and edge strength). Further, with the help of experiments, we have also established the fact that denoising before performing fusion gives effectively better results.

[1]  Alexander Toet,et al.  Towards cognitive image fusion , 2010, Inf. Fusion.

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

[3]  Baolong Guo,et al.  Multifocus Image Fusion Algorithm Based on Contourlet Decomposition and Region Statistics , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[4]  Somkait Udomhunsakul,et al.  Multi-focus Image Fusion Based on Stationary Wavelet Transform and Extended Spatial Frequency Measurement , 2009, 2009 International Conference on Electronic Computer Technology.

[5]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[6]  Cedric Nishan Canagarajah,et al.  Pixel- and region-based image fusion with complex wavelets , 2007, Inf. Fusion.

[7]  Ashish Khare,et al.  Edge Preserving Image Fusion Based on Contourlet Transform , 2012, ICISP.

[8]  Moongu Jeon,et al.  Multilevel adaptive thresholding and shrinkage technique for denoising using Daubechies complex wavelet transform , 2010 .

[9]  Subhasis Chaudhuri,et al.  A novel approach to quantitative evaluation of hyperspectral image fusion techniques , 2013, Inf. Fusion.

[10]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[11]  Stavri G. Nikolov,et al.  Image fusion: Advances in the state of the art , 2007, Inf. Fusion.

[12]  Cedric Nishan Canagarajah,et al.  Non-Gaussian model-based fusion of noisy images in the wavelet domain , 2010, Comput. Vis. Image Underst..

[13]  Xinyu Guo,et al.  Modeling Curled Leaves , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[14]  Vijanth S. Asirvadam,et al.  Image Enhancement by Fusion in Contourlet Transform , 2010 .

[15]  Yi Chai,et al.  Multifocus image fusion and denoising scheme based on homogeneity similarity , 2012 .

[16]  Cedric Nishan Canagarajah,et al.  Image Fusion Using Complex Wavelets , 2002, BMVC.

[17]  Zhongliang Jing,et al.  Review of pixel-level image fusion , 2010 .

[18]  Gonzalo Pajares Martinsanz,et al.  A wavelet-based image fusion tutorial , 2004 .

[19]  Richard Baraniuk,et al.  The Dual-tree Complex Wavelet Transform , 2007 .

[20]  Richa Singh,et al.  Multimodal Medical Image Fusion Using Redundant Discrete Wavelet Transform , 2009, 2009 Seventh International Conference on Advances in Pattern Recognition.

[21]  L. Yang,et al.  Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform , 2008, Neurocomputing.

[22]  Shutao Li,et al.  Performance comparison of different multi-resolution transforms for image fusion , 2011, Inf. Fusion.

[23]  杨波,et al.  Review of Pixel-Level Image Fusion , 2010 .

[24]  Ashish Khare,et al.  Soft-Thresholding for Denoising of Medical Images - a Multiresolution Approach , 2005, Int. J. Wavelets Multiresolution Inf. Process..