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A new image denoising technique in the wavelet transform domain for multiplicative noise is presented. Unlike most existing techniques, this approach does not require prior modeling of either the image or the noise statistics. It uses the variance of the detail wavelet coefficients to decide whether to smooth or to preserve these coefficients. The approach takes advantage of wavelet transform property in generating three detail subimages each providing specific information with certain feature directivity. This allows the ability to combine information provided by different detail subimages to direct the filtering operation. The algorithm uses the hypothesis test based on the F-distribution to decide whether detail wavelet coefficients are due to image related features or they are due to noise. The effectiveness of the proposed technique is tested for orthogonal as well as biorthogonal mother wavelets in order to study the effect of the smoothing process under different wavelet types.