We present here an effective scheme for image denoising based on total variation regularization. The proposed scheme allows to efficiently remove Poisson noise as well as Gaussian noise simultaneously with the help of a new kind of data fidelity term, suitable for the mixed Poisson-Gaussian noise model. The results show that the algorithm corresponding to our new scheme outperforms the existing methods for mixed Poisson-Gaussian noise removal.

S:In this paper, we present a computationally efficient technique for edge preserving in medical image smoothing, which is developed on the basis of dynamic programming multi-quadratic procedure. Additionally, we propose a new non-convex type of pair-wise potential functions, allow more flexibility to set a priori preferences, using different penalties for various ranges of differences between the values of adjacent image elements. The procedure of image analysis, based on the new data models, significantly expands the class of applied problems, and can take into account the presence of heterogeneities and discontinuities in the source data, while retaining high computational efficiency of the dynamic programming procedure and Kalman filterinterpolator. Comparative study shows, that our algorithm has high accuracy to speed ratio, especially in the case of high-resolution medical images.

In this paper, we present an effective algorithm for solving the Poisson-Gaussian total variation model. The existence and uniqueness of solution for the mixed Poisson-Gaussian model are proved. Due to the strict convexity of the model, the split-Bregman method is employed to solve the minimization problem. Experimental results show the effectiveness of the proposed method for mixed Poisson-Gaussion noise removal. Comparison with other existing and well-known methods is provided as well.

Introduction: A common problem in image restoration is image denoising. Among many noise models, the mixed Poisson-Gaussian model has recently aroused considerable interest. Purpose: Development of a model for denoising images corrupted by mixed Poisson-Gaussian noise, along with an algorithm for solving the resulting minimization problem. Results: We proposed a new total variation model for restoring an image with mixed Poisson-Gaussian noise, based on second-order total generalized variation. In order to solve this problem, an efficient alternating minimization algorithm is used. To illustrate its comparison with related methods, experimental results are presented, demonstrating the high efficiency of the proposed approach. Practical relevance: The proposed model allows you to remove mixed Poisson-Gaussian noise in digital images, preserving the edges. The presented numerical results demonstrate the competitive features of the proposed model.