In this paper, a new method is presented for reducing the image noise by wavelet transform. Wavelet thresholding is a standard method of reducing the signal noise in which the small coefficients are replace by zero and the big ones are either remain unchanged (hard thresholding) or reduced to the level of the threshold (soft thresholding). In the proposed method, for the first time, fuzzy kmeans clustering in each sub-band is used for choosing the threshold in soft thresholding method. Using fuzzy clustering, the coefficients in each sub-band are divided into three clusters, and then the noise cluster is obtained regarding the decomposition level and the maximum coefficient in each level. The upper and lower limit of the noisy cluster is an appropriate threshold for soft thresholding. This method is more efficient for reducing Gaussian and salt and pepper noises in comparison to methods that model the noise. In other words, the proposed method is not dependent on statistical noise or data driven is the manifest feature of the proposed approach relative to other methods and the threshold is selected based on type of images without each assumption on probability density function of noise. The experiments performed on basis images, show a higher performance of the proposed algorithm relative to the statistical method and the generalized cross validation method