{'fraction_non_alphanumeric': 0.018683274021352312, 'fraction_numerical': 0.0, 'mean_word_length': 5.181318681318682, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Novel Image Reconstruction Strategy for ECT: Combining Two Algorithms With a Graph Cut Method

Image reconstruction plays a key role in the application of electrical capacitance tomography (ECT). Although many different algorithms have been developed in the past, it is often difficult to obtain satisfactory images in all imaging regions by the use of a single algorithm due to the soft-field nature of ECT. This motivated us to develop a novel ECT image reconstruction strategy, in which a high-quality image can be obtained by combining the images reconstructed by two different algorithms via a graph cut method. By doing so, it is possible to retain the advantage of each algorithm for specified imaging regions and improve the quality of the whole image. This strategy was verified both numerically and experimentally for two widely used ECT reconstruction algorithms, i.e., linear backprojection and Tikhonov regularization. The results for stationary objects as well as gas–solid fluidized beds demonstrated that this graph-cut-based combination strategy presents a promising approach for ECT image reconstruction.