As a fast and non-intrusive measurement and visualization technique, Electrical Capacitance Tomography (ECT) is rapidly expanding its applications in the research on multiphase flow, fluidization, drying, combustion, and so on. However, the marked unevenness of the sensitivity maps sometimes causes unexpected effects in imaging reconstruction, particularly in three-dimension cases. To exploit the positive potential of this phenomenon, the authors proposed an image fusion method using the data from two units of ECT sensors in this study. This method is used in image fusion on the reconstructed images for a planar sensor and a cylindrical sensor. In contrast to the conventional fusion models that use fixed weight factors for two sources of data, our model forges weight functions that are set preference the strength of the sensitivity maps. The new algorithm is implemented by first extracting the characteristic information out of the ECT images using the Latent Low-Rank Representation and then performing a fusion algorithm with linear weight functions in preference to the significance of the sensitivity maps. The simulation results show that the algorithm effectively retains the advantages of the two units of sensors and mutually compensates the weak points of theirs, and significantly improves the reconstruction quality. The fusion image quality by the new method can response the real situation better in different heights. The results imply that the data fusion might to a significant extend amend the weakness of ECT caused by the uneven sensitivity maps.

In this study, we propose a new sensor structure with driving electrodes and a new excitation mode in electrical capacitance tomography (ECT) to improve: 1) the low sensitivity distribution in the central area caused by low potential distribution in the central area and 2) the nonuniform sensitivity distribution caused by the nonuniform potential distribution. An imaging method based on sensitivity map optimization is derived through electromagnetic field analysis and numerical simulation. In addition, the change rule of the optimal driving voltage is studied when the excitation voltage of the excitation electrodes and the length of the driving electrodes change. The numerical simulation and experimental results show that the imaging method proposed in this article can distinctively alter the potential distribution and sensitivity distribution in the central area by selecting the optimal driving voltage, which brings significant improvement in image reconstruction.

As an advanced detection technology in the industrial field, electrical capacitance tomography (ECT) can better reconstruct the material distribution state in the measured area by selecting the appropriate algorithm. In order to improve the reconstruction quality, this paper devises a novel objective function to model the ECT image reconstruction problem, in which L ₁ -norm is deployed as data fidelity with the focus on weakening the influence of capacitance outliers on the reconstruction quality, L _P regularization reinforces the sparseness of the phantom objects, and L ₁ regularization applies to model deviation variables to increase the robustness of the system. Based on the fast-iterative shrinkage thresholding technique and the soft thresholding method as sub-solvers, the split Bregman iterative method is designed as an effective solver for the proposed objective function. Numerical simulation and experimental validate that the proposed algorithm has excellent imaging ability compared with other imaging algorithms, which provides a good choice for the practical application of ECT in the future.

The effectiveness of infrared small target detection technology depends on high detection accuracy. However, when dealing with complex backgrounds or targets close to the edges of jamming objects, existing algorithms typically encounter significant challenges, such as high false alarm rates and low detection probabilities. To address this issue, we propose a novel measurement scheme utilizing joint local contrast measure based on background enhancement and edge suppression (JLCM-BEES), in which a new scanning window is employed to compute the joint local contrast of infrared images, with the focus on enhancing the target while suppressing the background, a new filtering kernel strengthens the background and suppresses the target, and two related structural elements are used for dilation and differencing operations on the infrared image, retaining the target while effectively suppressing edges with strong contrast. A new fusion method is introduced to combine the three obtained saliency maps, followed by the separation of the target from the background using an adaptive thresholding algorithm. Experimental results indicate that the proposed algorithm demonstrates superior detection capability and robustness when facing complex conditions and low signal-to-clutter ratios, providing new insights for the development of infrared small target detection technology.

Star trackers are susceptible to interference from stray light, such as sunlight, moonlight, and Earth atmosphere light, in the space environment, resulting in an overall improvement in the star image grayscale, poor background uniformity, low star extraction rate, and high number of false star spots. In response to these challenges, this paper proposes a grayscale iterative star spot extraction algorithm based on image entropy. The implementation of the algorithm is mainly divided into two steps: (1) The algorithm conducts multiple grayscale iterations, effectively utilizing the prior information on the local contrast of star spots to filter out stray light backgrounds to a certain extent. (2) By establishing an inner–outer template, the image entropy algorithm is employed to obtain the real star targets to be extracted, which further suppresses the background clutter and noise. Numerical simulations and experimental results demonstrate that, compared to traditional detection algorithms, this algorithm can effectively suppress background stray light, enhance star extraction rates, and reduce the number of false star spots, and it exhibits superior detection performance in complex backgrounds across various scenarios.

High-precision images enable electrical capacitance tomography (ECT) to obtain more reliable measurement results, meaning that the reconstruction algorithm is particularly important. Some excellent numerical algorithms have successfully solved the inverse problem for ECT imaging, but their imaging quality is relatively low. To solve this problem, this paper proposes a new reconstruction algorithm based on regularized extreme learning machines (RELMs). The implementation of the algorithm is mainly divided into two steps: (1) according to a large number of training samples, the RELM model can be obtained by the iterative split Bregman (ISB) algorithm, which can describe the mapping relationship between the capacitance correlation coefficient and the imaging target well, and (2) the capacitance correlation coefficient is calculated, which is then used as input to the RELM model to predict the final imaging. Both simulation and experimental results show that the RELM algorithm achieves greater improvement in imaging quality and robustness, and provides new development ideas for the ECT.