Search

Your search keyword '"Yamaguchi, Yusaku"' showing total 2 results
Start Over Author "Yamaguchi, Yusaku" Topic nonlinear differential equations
2 results on '"Yamaguchi, Yusaku"'

1. Continuous-time image reconstruction for binary tomography

Author

Yamaguchi, Yusaku, Fujimoto, Ken’ichi, Abou Al-Ola, Omar M., and Yoshinaga, Tetsuya

Subjects
*IMAGE reconstruction, *TOMOGRAPHY, *CONTINUOUS-time filters, *BINARY number system, *NONLINEAR differential equations, *IMAGE quality analysis
Abstract

Abstract: Binary tomography is the process of reconstructing a binary image from a finite number of projections. We present a novel method for solving binary tomographic inverse problems using a continuous-time image reconstruction (CIR) system described by nonlinear differential equations based on the minimization of a double Kullback–Leibler divergence. We prove theoretically that the divergence measure monotonically decreases in time. Moreover, we demonstrate numerically that the quality of the reconstructed images of the nonlinear CIR system is better than those from an iterative reconstruction method. [Copyright &y& Elsevier]

Published
2013
Full Text
View/download PDF

2. Noise-Robust Image Reconstruction Based on Minimizing Extended Class of Power-Divergence Measures.

Author

Kasai, Ryosuke, Yamaguchi, Yusaku, Kojima, Takeshi, Abou Al-Ola, Omar M., and Yoshinaga, Tetsuya

Subjects
*IMAGE reconstruction algorithms, *IMAGE reconstruction, *COMPUTED tomography, *NONLINEAR differential equations, *NONLINEAR equations, *TOMOGRAPHY, *ALGORITHMS
Abstract

The problem of tomographic image reconstruction can be reduced to an optimization problem of finding unknown pixel values subject to minimizing the difference between the measured and forward projections. Iterative image reconstruction algorithms provide significant improvements over transform methods in computed tomography. In this paper, we present an extended class of power-divergence measures (PDMs), which includes a large set of distance and relative entropy measures, and propose an iterative reconstruction algorithm based on the extended PDM (EPDM) as an objective function for the optimization strategy. For this purpose, we introduce a system of nonlinear differential equations whose Lyapunov function is equivalent to the EPDM. Then, we derive an iterative formula by multiplicative discretization of the continuous-time system. Since the parameterized EPDM family includes the Kullback–Leibler divergence, the resulting iterative algorithm is a natural extension of the maximum-likelihood expectation-maximization (MLEM) method. We conducted image reconstruction experiments using noisy projection data and found that the proposed algorithm outperformed MLEM and could reconstruct high-quality images that were robust to measured noise by properly selecting parameters. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results