Signal-dependent noise removal in the undecimated wavelet domain

In this paper, methods to denoise images corrupted by a signal-dependent additive distortion are proposed. The noise model is parametric to take into account different noise generation processes. Noise reduction is approached as a Wiener-like filtering performed in a shift-invariant wavelet domain by means of an adaptive rescaling of the coefficients of an undecimated decomposition. The scaling factor is computed by using the statistics estimated from the degraded image and the parameters of the noise model. The absence of decimation in the wavelet decomposition avoids the ringing impairments produced by critically-subsampled wavelet-based denoising. Experimental results demonstrate that excellent background smoothing as well as preservation of edge sharpness and texture can be obtained.