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In particular, the efficiency of this method relies on the performance of inner solvers for the resulting subproblems. The speckle is removed while edges and structural details of the image are preserved.īregman methods introduced in to image processing are demonstrated to be an efficient optimization method for solving sparse reconstruction with convex functionals, such as the ℓ1 norm and total variation. Quantitative results on synthetic and real images have demonstrated the efficiency and the robustness of the proposed method compared to well-established and state-of-the-art methods.
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Instead of performing the smoothing uniformly, the process is achieved in preferred orientations, more in homogeneous areas than in detailed ones to preserve region boundaries while reducing speckle noise within regions. The de-noising process is performed using a multiplicative regularization method through an adaptive window whose shapes, sizes and orientations vary with the image structure. model and a Weighted Total Variation (WTV) function as a multiplicative factor in the cost functional. This method combines a data misfit function based on Loupas et al. In order to solve the difficulty of designing a filter for an effective speckle removing, we propose a new approach for de-noising images while preserving important features. Ultrasound images are corrupted by a multiplicative noise - the speckle - which makes hard high level image analysis.