Abstract
In most applications of optical computed tomography (OpCT), limited-view problems are often encountered, which can be solved to a certain extent with typical OpCT reconstructive algorithms. The concept of entropy first emerged in information theory has been introduced into OpCT algorithms, such as maximum entropy (ME) algorithms and cross entropy (CE) algorithms, which have demonstrated their superiority over traditional OpCT algorithms, yet have their own limitations. A fused entropy (FE) algorithm, which follows an optimized criterion combining self-adaptively ME with CE, is proposed and investigated by comparisons with ME, CE and some traditional OpCT algorithms. Reconstructed results of several physical models show this FE algorithm has a good convergence and can achieve better precision than other algorithms, which verifies the feasibility of FE as an approach of optimizing computation, not only for OpCT, but also for other image processing applications.
Highlights
Optical computed tomography (OpCT) techniques such as interferometry tomography [1,2,3], light beam deflection tomography [4], emission spectral tomography [5,6,7], etc., are a branch of computed tomography (CT), which is mainly applied to optical testing of 3-D distributions of physical variables of a number of fluid fields [8,9,10]
The performance of fused entropy (FE) is investigated by comparisons with maximum entropy (ME), cross entropy (CE) and some traditional OpCT
The performance of FE is investigated by comparisons with ME, CE and two traditional OpCT
Summary
Optical computed tomography (OpCT) techniques such as interferometry tomography [1,2,3], light beam deflection tomography [4], emission spectral tomography [5,6,7], etc., are a branch of computed tomography (CT), which is mainly applied to optical testing of 3-D distributions of physical variables of a number of fluid fields [8,9,10]. OpCTs encounter limited-view problems [11,12,13], e.g., incomplete testing views and/or incomplete data at each view, which results in worse reconstruction precision and lower spatial resolution than that seen in medical CT. To solve this problem, many OpCT algorithms have been developed since. Aderson [16] proposed a simultaneous algebraic reconstruction technique (SART) algorithm, which combined the advantages of ART and SIRT. Some multi-criterion OpCT algorithms [13] have been proposed, their reconstruction results are still unsatisfactory when the distribution of tested fields is relatively complex. Results of numerical simulations show this FE has a good convergence and a better precision than other algorithms
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