Multi-Focus Image Fusion Using Block-Wise Color-Principal Component Analysis

In this work, multi-focus image fusion method has been proposed by using color-principal component analysis (C-PCA). Proposed method consists of different phases. In the first phase, both source images have been converted into three RGB color channels. In the next phase, for each channel, covariance's has been calculated for both images. Special weights have been calculated to generate intermediate images. In the next phase, Convolution has been used with Gaussian blur to make image smooth. Zero-crossing based second order-derivative has been incorporated to calculate edges. In the last phase, images have been decomposed into blocks. Salient features information by using Laplacian of Gaussian and Spatial Frequency of each block have been used to get the fused image. Experimental results show that the proposed method performs well as compare to existing methods by using quality matrices.