Multifocus Image Reconstruction and Fusion for 3-D Flow Visualization Using an Optimized Four-Plane ECT Sensor

3D image reconstruction of permittivity distribution is very challenging for electrical capacitance tomography (ECT) because of the limited number of capacitance measurements and the non-uniformly distributed sensitivity, especially for axial distribution. In this paper, a 3D image reconstruction method based on fuzzy adaptive Kalman filter (FAKaF) and multi-focus image fusion is used to improve the 3D image quality. A four-plane ECT sensor is constructed and optimized based on the conformal transformation method, whose sensitivity in the imaging volume is more uniform in the axial direction. Then a FAKaF is established to obtain a series of initial reconstructed images with higher image quality in the central region, which can be seen as a sequence of multi-focus 3D images. Finally, an image registration and fusion method based on cross correlation and spatial frequency is used to extract and fuse the high-quality parts of each image in the sequence to further improve the quality of the reconstructed images. Simulations and experiments were carried out to evaluate the performance of the proposed method. The results show that compared with the widely-used methods, initial images reconstructed using the proposed FAKaF-based method possess higher image quality. Through image fusion, higher correlation coefficients between reference distributions and fused images can be obtained, proving the effectiveness of the proposed method.