Image Reconstruction of ERT Center Region Based on Improved Spatial Adaptive Regularization Algorithm

• Published in: 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) pp. 1 - 6
• Main Authors: Lu, Qian, Ren, Hongwei, Han, Yubao, Dong, Xingkun, Cheng, Xin, Qin, Lei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 05.11.2022
• Subjects: Biomedical measurement; Center Region Imaging; Electrical resistance measurement; Improved Spatial Adaptive Regularization Algorithm; Normalization; Sensitivity; Signal processing algorithms; Simulation; Sparse matrices; Threshold Correction; Tomography
• DOI: 10.1109/CISP-BMEI56279.2022.9979908

In order to improve the image reconstruction quality of electrical resistance tomography (ERT) system, after comparing the imaging effects of defects in different regions, the central region with poor imaging results was improved. Firstly, the data is normalized and preprocessed to make the sensitivity matrix more uniform, which improves the problem of poor central sensitivity and strong boundary sensitivity of the measurement field, and improves the imaging effect of the central field to a certain extent. Due to the sparse nature of the measured data and reconstructed images of the resistance tomography system, an improved spatially adaptive regularization algorithm is proposed, which can effectively segment different media in the target field by correlating the shrinkage factor with the matrix sparsity, Improve image quality. In order to further improve the imaging resolution and eliminate the artifacts between the imaging background and the target, appropriate target and background threshold corrections are added to the improved algorithm. Finally, the effectiveness of the method is verified by simulation. The simulation results show that the average correlation coefficient after improvement is 43.3% higher than that before improvement.