A Novel Multi-Focus Image Fusion Method Based on Stationary Wavelet Transform and Local Features of Fuzzy Sets

The key issue of multi-sensor image fusion is how to accurately extract and fuse the high-quality pixels or coefficients of source images. Nevertheless, the so-called high-quality is an uncertain or fuzzy definition, which is very suitable for fuzzy theory to address this problem. By the integration of stationary wavelet transform (SWT) and fuzzy sets, this paper proposes a new multi-focus image fusion scheme, which can merge the important features of different source images into a fused image. First, the source images are decomposed by SWT to get a set of sub-images with different detailed features. Second, the Gaussian membership function (GMF) is utilized to get the fuzzy sets of sub-images data. Third, the local spatial frequency (LSF) is employed to extract the local features of the sub-images by the generated fuzzy sets. At last, the fusion rule is designed based on consistency verification to fuse the sub-images according to the LSF of fuzzy sets, and then inverse SWT (ISWT) is implemented to reconstruct the fused image. The experimental and contrastive results of different image sets show that the proposed method is an effective multi-focus image fusion scheme which can achieve better fusion effect than other methods.