  In many applications, Image de-noising and improvement represent essential
processes in presence of colored noise such that in underwater. Power spectral
density of the noise is changeable within a definite frequency range, and
autocorrelation noise function is does not like delta function. So, noise in
underwater is characterized as colored noise. In this paper, a novel image
de-noising method is proposed using multi-level noise power estimation in
discrete wavelet transform with different basis functions. Peak signal to noise
ratio (PSNR) and mean squared error represented performance measures that the
results of this study depend on it. The results of various bases of wavelet
such as: Daubechies (db), biorthogonal (bior.) and symlet (sym.), show that
denoising process that uses in this method produces extra prominent images and
improved values of PSNR than other methods.
