Multi-focus Image Fusion Using Stationary Wavelet Transform (SWT) with Principal Component Analysis (PCA)

Multi-focus image fusion is that combining two or more source images which have same scenes but different focuses. All in focus image is more informative so it can be processed easily. Multi-focus image fusion is used different areas such as; health system, wsn, etc. We proposed a new hybrid method using Stationary Wavelet Transform (SWT) with Principal Component Analysis (PCA). This method uses transform domain. We used SWT for feature extraction. SWT decompose image four different subbands. After extraction feature, to combine images we proposed PCA based fusion rule. With PCA from subbands of source images are computed eigenvectors and selected maximum eigenvector of this sub-bands because maximum eigenvector represents image ideally. After application fusion rule, we got four new subbands and reconstructed new all in focus image using this subbands with inverse SWT. Mutual Information, Standard Deviation, Spatial Frequency and Petrovic’s Metric are used as quality metrics.