Image denoising using fuzzy set function

The dual-tree complex wavelet transform has been proposed as a novel analysis tool featuring near shift-invariance and improved directional selectivity compared to the standard wavelet transform. Within this framework, we describe a novel technique for removing AWGN, additive white Gaussian noise, from digital image. In this paper, we design multivariate maximum a posterior (MAP) estimator, which relies on the fuzzy sets. In fact, the fuzzy sets is similar to the probability density function (PDF). Fuzzy sets can have any shape. Here, we test our algorithm for the modified Sinc function case. The experimental results show that the proposed method yields good denoising results.