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In this paper, an algorithm for multifocus image fusion is presented. In this algorithm, the discrete multiwavelet transform (DMWT) has been used as decomposition tool to obtain multiresolution data. A major advantage of this method over discrete wavelet transform (DWT) is its sensitivity to different kinds of object features in digital images. The developed algorithm consists of a registration step followed by a decomposition step using DMWT. Then, an optimized coefficient selection algorithm consisting of coefficient grouping and consistency verification is used. Finally, the fused image is obtained by reconstruction of the selected coefficients using inverse DMWT. The algorithm has been applied to some multifocus images. Quantitative and qualitative analyses of the results demonstrate higher performance of the algorithm compared to the same method using DWT.

This paper represents a new region-based active contour model that can be used to segment images with intensity non-uniformity and high-level noise. The main idea of our proposed method is to use Gaussian distributions with different means and variances with incorporation of intensity non-uniformity model for image segmentation. In order to integrate the spatial information between neighboring pixels in our proposed method, we use Markov Random Field. Our experiments on synthetic images and cerebral magnetic resonance images show the advantages of the proposed method over state-of-art methods, i.e. local Gaussian distribution fitting.