Nonlocal Means Filtering Based Speckle Removal Utilizing the Maximum <italic>a Posteriori</italic> Estimation and the Total Variation Image Prior

Abstract

In this paper, the problem of image speckle removal is addressed. To alleviate the pepper-salt remainder in the speckled image, we propose to utilize the nonlocal means filtering, where the weighting coefficients are derived based on the maximum a posteriori estimation with the total variation image prior. As a result, the objective function of the pixel fitting term plus the total variation regularizer is formulated, and it is solved with the majorization-minimization approach. To avoid the computationally intractable step size selection in the huge-scale gradient-based optimization, we split and solve the variables in the pixel fitting term and regularizer by means of the alternating direction method of multipliers. Performance analysis is performed for the Rayleigh and Gamma distributed signal models. The simulation and experimental results show the superior performance compared with other image despeckling methods in terms of various metrics and visual perception.

Keywords

• Nonlocal means filtering
• speckle
• a posteriori estimation%22%2C%22default_operator%22%3A%22AND%22%7D%7D%7D"> maximum <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">a posteriori</italic> estimation
• total variation image prior
• majorization-minimization approach
• alternating direction method of multipliers