A new hybrid fusion technique for grayscale images & comparative analysis

Image Fusion is a process of combining the relevant information from a set of images into a single image, where the resultant fused image will be more informative and complete than any of the input images. The Proposed image fusion technique includes following steps: first, discrete wavelet transform is applied to obtain the wavelet coefficients of the source images. The coefficients are processed with different fusion rules like maximum pixel, average pixel and region based masking and addition selection rule to get the primary fused image, which is again processed with the region based fusion rules to get the secondary fused image. The primary and secondary images are processed again with most efficient fusion rule Principal Component Analysis to get the final fused image. The performance of the proposed image fusion scheme is evaluated with peak to signal noise ratio (PSNR), Normalized cross correlation (NCC), entropy (EN). The fusion performance of the proposed method compared with some common individual DWT and PCA Method.