Abstract
In this paper, we proposed an algorithm to find the optimal threshold value for denoising an image. A new cost function is designed to find the optimal threshold in every image. The cost function is based on the intuitionistic fuzzy divergence measure of the denoised image and original image. In addition, the intuitionistic fuzzy entropy of denoised image is added to the cost function. This is necessary, because when the algorithm threshold value is decreased, the denoised image is blurred, although its divergence of original image is decreased. When the value of intutionistic fuzzy entropy and intutionistic fuzzy divergence measure are minimum, the sum that is the cost value will also minimum. The threshold for image denoising with a minimum cost value will be the optimal threshold for image denoising. The implementation and applicability of the proposed algorithm have been illustrated by taking different sample images. The obtained results have been finally analyzed and found to be better than the existing ones.
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