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Mathematical Problems in Engineering
Volume 2016, Article ID 5637306, 12 pages
http://dx.doi.org/10.1155/2016/5637306
Research Article

Multifocus Image Fusion in Q-Shift DTCWT Domain Using Various Fusion Rules

1School of Mechatronic Engineering and Automation, Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200072, China
2Shanghai Electric Group Co., Ltd., Shanghai 200072, China
3Shanghai Electrical Apparatus Research Institute (Group) Co., Ltd., Shanghai 200063, China

Received 20 April 2016; Revised 31 August 2016; Accepted 25 September 2016

Academic Editor: Sergio Teggi

Copyright © 2016 Yingzhong Tian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Multifocus image fusion is a process that integrates partially focused image sequence into a fused image which is focused everywhere, with multiple methods proposed in the past decades. The Dual Tree Complex Wavelet Transform (DTCWT) is one of the most precise ones eliminating two main defects caused by the Discrete Wavelet Transform (DWT). Q-shift DTCWT was proposed afterwards to simplify the construction of filters in DTCWT, producing better fusion effects. A different image fusion strategy based on Q-shift DTCWT is presented in this work. According to the strategy, firstly, each image is decomposed into low and high frequency coefficients, which are, respectively, fused by using different rules, and then various fusion rules are innovatively combined in Q-shift DTCWT, such as the Neighborhood Variant Maximum Selectivity (NVMS) and the Sum Modified Laplacian (SML). Finally, the fused coefficients could be well extracted from the source images and reconstructed to produce one fully focused image. This strategy is verified visually and quantitatively with several existing fusion methods based on a plenty of experiments and yields good results both on standard images and on microscopic images. Hence, we can draw the conclusion that the rule of NVMS is better than others after Q-shift DTCWT.