Haytham A. Ali^1,2,*, Hiroyuki Kudo¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3741-3756, 2022, DOI:10.32604/cmc.2022.029394

Abstract This paper proposes a new level-set-based shape recovery approach that can be applied to a wide range of binary tomography reconstructions. In this technique, we derive generic evolution equations for shape reconstruction in terms of the underlying level-set parameters. We show that using the appropriate basis function to parameterize the level-set function results in an optimization problem with a small number of parameters, which overcomes many of the problems associated with the traditional level-set approach. More concretely, in this paper, we use Gaussian functions as a basis function placed at sparse grid points to represent the parametric level-set function and… More >

Xi Chen¹, Yanpeng Wu^2,*, Chenxue Zhu², Hongjun Liu³

Journal of New Media, Vol.4, No.3, pp. 117-123, 2022, DOI:10.32604/jnm.2022.027060

Abstract The Hessian matrix has a wide range of applications in image processing, such as edge detection, feature point detection, etc. This paper proposes an image enhancement algorithm based on the Hessian matrix. First, the Hessian matrix is obtained by convolving the derivative of the Gaussian function. Then use the Hessian matrix to enhance the linear structure in the image. Experimental results show that the method proposed in this paper has strong robustness and accuracy. More >