Abstract:

Aiming at the feature of low resolution and faint contrast for infrared images, a segmentation algorithm is presented based on the contourlet transform and fuzzy c-means clustering. This method assumes that the prior distribution of the original image coefficients and the noise coefficients both are Gaussian and applies the proportional shrinkage based on the rule of maximum a posteriori to the infrared image’s denoising in contourlet domain. Subsequently, an improved fuzzy c-means clustering algorithm is put forward to segment the denoised image. This method improves the segmented performance in three aspects which are the method of the minimum-maximum distance based on the histogram to compute the original clustering center, the computing of the sample weight and the revising of the membership grade during the clustering procedure by considering the pixel’s neighbor.  The experimental results show that the proposed method, compared with the standard algorithm, can make the partition entropy decreased by 10% and the region contrast ratio increased by 27%. So this algorithm can segment the infrared image which is polluted by noise effectively.