Quality Enhancement of Multifocus & Medical Images Using Hybrid Wavelets Based Fusion for Bioengineering Applications

Abstract

Multifocus image fusion employs fusion principles to integrate many focused images of the same scene. All-in-focus images are instructive and valuable for visual perception. Maintaining shift-invariant and directional selectivity in a fused image is crucial. Traditional wavelet-based fusion methods are hindered their performance due to a lack of invariant shift and reduced directionality. In this paper, a classical multifocus hybrid wavelet-based approach with principal component analysis (PCA) is proposed. At the first level of decomposition, stationary wavelet transformation (SWT) is used to perform the fusion process with the given source images. In the next level, approximation coefficients of source images are selected for decomposition as well as fusion using dual-tree complex wavelet transformation (DTCWT) and finally, PCA is applied to generate a final fused image. Analysis of the proposed method has been accomplished by evaluating various objective parameters.