Maximum Local Energy Based Multifocus Image Fusion in Mirror Extended Curvelet Transform Domain

• Published in: 2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing pp. 799 - 802
• Main Authors: Lifeng Zhang, Huimin Lu, Yujie Li, Serikawa, S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2012
• Subjects: discrete cosine transform; Image fusion; Indexes; maximum local energy; mirror extended curvelet transform; Mirrors; PSNR; Sensors; Wavelet transforms
• ISBN: 1467321206; 9781467321204
• DOI: 10.1109/SNPD.2012.16

In this paper, we firstly propose the maximum local energy (MLE) method to calculate the low frequency coefficients of images and compare the results with those of mirror extended curve let transform, which enhance the edge features and details of images. An image fusion step was performed as follows: First, we obtained the coefficients of two different types of images through mirror extended curve let transform. Second, we selected the low frequency coefficients by maximum local energy and obtaining the high-frequency coefficients using the absolute maximum value (AMV) method. Finally, the fused image was obtained by performing an inverse mirror extended curve let transform. In addition to human vision analysis, the images were also compared through quantitative analysis. multifocus images were used in the experiments to compare the results among the beyond wavelets. The numerical experiments reveal that maximum local energy is a new strategy for attaining image fusion with satisfactory performance.