Kaczmarz extended algorithm for tomographic image reconstruction from limited-data
Mathematics and Computers in Simulation
Limited Data X-Ray Tomography Using Nonlinear Evolution Equations
SIAM Journal on Scientific Computing
Sparse reconstruction by separable approximation
IEEE Transactions on Signal Processing
Adaptive wavelet-Galerkin methods for limited angle tomography
Image and Vision Computing
IEEE Transactions on Information Theory
A moment-based variational approach to tomographic reconstruction
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration
IEEE Transactions on Image Processing
Local tomography based on grey model
Neurocomputing
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This paper focuses on the problem of incomplete data in the applications of the circular cone-beam computed tomography. This problem is frequently encountered in medical imaging sciences and some other industrial imaging systems. For example, it is crucial when the high density region of objects can only be penetrated by X-rays in a limited angular range. As the projection data are only available in an angular range, the above mentioned incomplete data problem can be attributed to the limited angle problem, which is an ill-posed inverse problem. This paper reports a modified total variation minimisation method to reduce the data insufficiency in tomographic imaging. This proposed method is robust and efficient in the task of reconstruction by showing the convergence of the alternating minimisation method. The results demonstrate that this new reconstruction method brings reasonable performance.