Conference Paper

# Poisson image reconstruction with total variation regularization

Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA

DOI: 10.1109/ICIP.2010.5649600 Conference: Image Processing (ICIP), 2010 17th IEEE International Conference on Source: IEEE Xplore

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**ABSTRACT:**We propose a flexible and computationally efficient method to solve the non-homogeneous Poisson (NHP) model for grayscale and color images within the TV framework. The NHP model is relevant to image restoration in several applications, such as PET, CT, MRI, etc. The proposed algorithm uses a quadratic approximation of the negative log-likelihood function to pose the original problem as a non-negative quadratic programming problem. The reconstruction quality of the proposed algorithm outperforms state of the art algorithms for grayscale im-age restoration corrupted with Poisson noise. Furthermore, it places no prohibitive restriction on the forward operator, and to best of our knowledge, the proposed algorithm is the only one that explicitly includes the NHP model for color images within the TV framework.04/2011; - [Show abstract] [Hide abstract]

**ABSTRACT:**We propose a flexible and computationally efficient method to solve the non-homogeneous Poisson (NHP) model for grayscale and color images within the TV framework. The NHP model is relevant to image restoration in several applications, such as PET, CT, MRI, etc. The proposed algorithm uses a novel method to spatially adapt the regularization parameter; it also uses a quadratic approximation of the negative log-likelihood function to pose the original problem as a non-negative quadratic programming problem. The reconstruction quality of the proposed algorithm outperforms state of the art algorithms for grayscale image restoration corrupted with Poisson noise. Moreover, it places no prohibitive restriction on the forward operator, and to best of our knowledge, the proposed algorithm is the only one that explicitly includes the NHP model for color images and that spatially adapts its regularization parameter within the TV framework.01/2011; - [Show abstract] [Hide abstract]

**ABSTRACT:**Computerized tomography (CT) plays an important role in medical imaging, especially for diagnosis and therapy. However, higher radiation dose from CT will result in increasing of radiation exposure in the population. There-fore, the reduction of radiation from CT is an essential issue. Expectation maximization (EM) is an iterative method used for CT image reconstruction that maximizes the likelihood function under Poisson noise assump-tion. Total variation regularization is a technique used frequently in image restoration to preserve edges, given the assumption that most images are piecewise constant. Here, we propose a method combining expectation maximization and total variation regularization, called EM+TV. This method can reconstruct a better image using fewer views in the computed tomography setting, thus reducing the overall dose of radiation. The numeri-cal results in two and three dimensions show the efficiency of the proposed EM+TV method by comparison with those obtained by filtered back projection (FBP) or by EM only.Proc SPIE 03/2011;

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