Multivalued Discrete Tomography Using Dynamical System That Describes Competition

Abstract

Multivalued discrete tomography involves reconstructing images composed of three or more gray levels from projections. We propose a method based on the continuous-time optimization approach with a nonlinear dynamical system that effectively utilizes competition dynamics to solve the problem of multivalued discrete tomography. We perform theoretical analysis to understand how the system obtains the desired multivalued reconstructed image. Numerical experiments illustrate that the proposed method also works well when the number of pixels is comparatively high even if the exact labels are unknown.

Polychromatic CT Data Improvement with One‐Parameter Power Correction

“…However, nowadays algorithm development work is in progress due to the fact that the objects with nonstandard attenuation properties [9] have to be measured or nonstandard experimental conditions are required (lowdose tomography) [10]. The works are focused also on an improvement of the algorithms properties like convergence [11,12] or the quality of the reconstruction [13].…”