Shaoyuan Sun

A region-based image fusion algorithm using multiresolution segmentation

This paper describes a region-based multiresolution (MR) image fusion algorithm. The MR segmentations of source images to be fused are obtained by fuzzy c-means clustering. From these MR region representations of all the input images, we obtain a shared MR region representation to label all the input images. The fusion process is guided by these MR region representations. Experiment results using real data show that the proposed algorithm works well in multisensor image fusion.

Pixel visibility based multifocus image fusion

Due to the limited depth-of-field of optical lenses, it is difficult to get an image with all objects in focus. One way to overcome this problem is to take several images with different focus points and combine them into a single composite which contains all the regions full focused. This paper describes a pixel visibility based multifocus image fusion algorithm. For each pixel in the source multifocus images, the pixel visibility is calculated. The fusion procedure is performed by a selection mode according to the magnitude of pixel visibility. Experiments show that the proposed algorithm works well in multifocus image fusion.

Image fusion based on estimation theory

An image fusion method is proposed based on a kind of estimation theory EM (expectation maximization) algorithm and discrete wavelet frame for merging multiple sensor images. The registered images are firstly decomposed by discrete wavelet frame. Secondly since the fusion of the lowest frequency band is crucial to the final result, they are fused effectively by EM algorithm. The high frequency bands are fused by selection method involving the informative importance measure. Then the fused image is achieved by the discrete wavelet frame invert transform. Experimental results indicate that the proposed method outperforms the discrete wavelet transform and the existing image fusion methods.

Infrared image enhancement based on two-dimensional histogram analysis

In this paper, a novel image enhancement technique suitable for infrared image, dualistic sub-image enhancement based on two-dimensional histogram analysis and histogram equalization, is put forward. Firstly, the infrared image is segmented to two sub-images according to the correlation between the neighboring pixels, which is based on the two-dimensional histogram analysis. Then each sub-image is enhanced based on histogram equalization. At last, we get the result after the processed sub-images are composed into one image. The experiment result indicates that the algorithm can not only enhance image information effectively but also keep the fine part of original infrared image well. And this algorithm eliminates the drawback of traditional histogram equalization that the fine part is not easy to control and protect.