results without blurring. It is shown that for the DWT a bigger number of the input parameters (like the wavelet’s choice, number of the iteration, degree of the decomposition, kind of the smoothing thresholding etc.) is needed, what makes it more difficult to optimize the output image. The analysis of the Signal to Noise Ratio (SNR) and the Contrast to Noise Ratio (CNR) and edge quality detection for both methods is presented to show their effectiveness. We concluded that the AWS algorithm can be applied to improve MRI images mainly in the case of high noise and low Signal to Noise Ratio (SNR) whereas wavelet transform is effective in any case. The wavelet transform application provides additional possibilities like image compression and image fusion, which can be useful in the MRI.