Multi-focus image fusion means to fuse multiple source images with different focus settings into one image, so that the resulting image appears sharper. In order to extract the focused regions of the fused image efficiently, a novel pulse coupled neural network (PCNN) method for multi-focus image fusion is proposed. The registered source images are decomposed into principal components and sparse components by robust principal component analysis (RPCA) decomposition, and the important features of the sparse components are used to motivate the PCNN neurons, whose outputs detect the focused regions of the source images and integrate them to construct the final fused image. Experimental results show that the proposed scheme works better in extracting the focused regions and improving the fusion quality compared to the other existing fusion methods in terms of mutual information (MI) and

The paper introduces a novel algorithm to determine the optimal decomposition level in wavelet de-noising. The algorithm selects the optimal decomposition level by comparing the sparsity of wavelet coefficients at adjacent levels. The level whose wavelet coefficient has the maximum sparsity can be confirmed as the optimal decomposition level. We demonstrate experimentally that wavelet de-noising performs better using optimal decomposition level determined by our proposed algorithm than White Noise Test (WNT) method and Maximum Energy (ME) method.