Methods for image noise reduction based on wavelet analysis perform by first decomposing the image and then by applying non-linear compression functions on the wavelet components. The approach commonly used to reduce the noise is to threshold the absolute pixel values of the components. The thresholding functions applied are members of a family of functions defining a specific shape. This shape has a fundamental influence on the characteristics of the output image. This work presents and tests an alternative shape deduced from statistical estimation. Optimal shapes are deduced using Bayesian theory and a new shape is defined to approximate them. The derivation of thresholding shapes is optimal in LMSE and MAP senses. The noise is assumed additive Gaussian and white (AWGN) and the components are assumed to have statistical distributions consistent with the real component distributions. The optimal shapes are then approximated by a scheme utilised in the noise reduction procedure. Results demonstrating the efficiency of the image noise reduction procedure are included in the work.
['A. De Stefano', 'Paul R. White', 'W.B. Collis']
Selection of Thresholding Scheme for Image Noise Reduction on Wavelet Components Using Bayesian Estimation