Fusion of multifocus images by lattice structures

A new image fusion algorithm based on 1-D lattice filter structures is proposed.Undecimated lattice structure is developed for image decomposition/reconstruction.Undecimated lattice structure is applied to multifocus image fusion.Proposed method is better than the classical wavelet based methods. Image fusion methods based on multiscale transform (MST) suffer from high computational load due to the use of fast Fourier transforms (ffts) in the lowpass and highpass filtering steps. Lifting wavelet scheme which is based on second generation wavelets has been proposed as a solution to this issue. Lifting Wavelet Transform (LWT) is composed of split, prediction and update operations all implemented in the spatial domain using multiplications and additions, thus computation time is highly reduced. Since image fusion performance benefits from undecimated transform, it has later been extended to Stationary Lifting Wavelet Transform (SLWT). In this paper, we propose to use the lattice filter for the MST analysis step. Lattice filter is composed of analysis and synthesis parts where simultaneous lowpass and highpass operations are performed in spatial domain with the help of additions/multiplications and delay operations, in a recursive structure which increases robustness to noise. Since the original filter is designed for the undecimated case, we have developed undecimated lattice structures, and applied them to the fusion of multifocus images. Fusion results and evaluation metrics show that the proposed method has better performance especially with noisy images while having similar computational load with LSWT based fusion method.

[1]  Yi Chai,et al.  Multifocus image fusion scheme based on features of multiscale products and PCNN in lifting stationary wavelet domain , 2011 .

[2]  Chang-Soo Lee,et al.  New lifting based structure for undecimated wavelet transform , 2000 .

[3]  屈小波 Xiaobo Qu,et al.  Image Fusion Algorithm Based on Spatial Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain , 2008 .

[4]  Jie-Lin Zhang,et al.  Fusion of multispectral and panchromatic satellite images based on ihs and curvelet transformations , 2007, 2007 International Conference on Wavelet Analysis and Pattern Recognition.

[5]  Yaonan Wang,et al.  Multifocus image fusion using artificial neural networks , 2002, Pattern Recognit. Lett..

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

[7]  Qiang Zhang,et al.  Fusion of Multi-sensor Images Based on the Nonsubsampled Contourlet Transform: Fusion of Multi-sensor Images Based on the Nonsubsampled Contourlet Transform , 2009 .

[8]  Vladimir S. Petrovic,et al.  Gradient-based multiresolution image fusion , 2004, IEEE Transactions on Image Processing.

[9]  Wim Sweldens,et al.  The lifting scheme: a construction of second generation wavelets , 1998 .

[10]  Shutao Li,et al.  Multifocus image fusion using region segmentation and spatial frequency , 2008, Image Vis. Comput..

[11]  Alexander Toet,et al.  Merging thermal and visual images by a contrast pyramid , 1989 .

[12]  Marc Moonen,et al.  Joint DOA and multi-pitch estimation based on subspace techniques , 2012, EURASIP J. Adv. Signal Process..

[13]  Gonzalo Pajares,et al.  A wavelet-based image fusion tutorial , 2004, Pattern Recognit..

[14]  Yongdong Zhang,et al.  A Highly Parallel Framework for HEVC Coding Unit Partitioning Tree Decision on Many-core Processors , 2014, IEEE Signal Processing Letters.

[15]  Lei Zhang,et al.  Noise Reduction for Magnetic Resonance Images via Adaptive Multiscale Products Thresholding , 2003, IEEE Trans. Medical Imaging.

[16]  Yongdong Zhang,et al.  Efficient Parallel Framework for HEVC Motion Estimation on Many-Core Processors , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[17]  Dennis M. Healy,et al.  Wavelet transform domain filters: a spatially selective noise filtration technique , 1994, IEEE Trans. Image Process..

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

[19]  P. P. Vaidyanathan,et al.  Lattice structures for optimal design and robust implementation of two-channel perfect-reconstruction QMF banks , 1988, IEEE Trans. Acoust. Speech Signal Process..

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

[21]  W. Brent Seales,et al.  Everywhere-in-focus image fusion using controlablle cameras , 1996, Other Conferences.

[22]  Jason Jianjun Gu,et al.  Multi-focus image fusion using PCNN , 2010, Pattern Recognit..

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

[24]  Yi Chai,et al.  Multifocus image fusion scheme based on feature contrast in the lifting stationary wavelet domain , 2012, EURASIP Journal on Advances in Signal Processing.

[25]  B. S. Manjunath,et al.  Multi-sensor image fusion using the wavelet transform , 1994, Proceedings of 1st International Conference on Image Processing.

[26]  D. Donoho,et al.  Translation-Invariant De-Noising , 1995 .

[27]  Guo Bao Fusion of Multi-sensor Images Based on the Nonsubsampled Contourlet Transform , 2008 .