ECT-EMT Image Fusion Based on Cross-Sensitive Field Optimization and Super-Resolution

In the oil and gas related fields, three-phase distribution flow in pipelines is one of the important aspects that need to be continuously monitored in the Industrial Pipeline Internet of Things (IoT). The dual-modality system of Electrical Capacitance Tomography (ECT) and Electromagnetic Tomography (EMT) can reconstruct the three-phase distribution within a pipeline’s cross-section. However, due to mutual interference among three-phase flow media and noise effects, the reconstruction quality of ECT and EMT images is degraded. In this paper, an ECT-EMT image fusion method based on cross-sensitive field optimization and super-resolution preprocessing is proposed. Firstly, unimodal ECT image reconstruction generates virtual image in solid-phase fluid regions, leveraging this information, a cross-sensitive field optimization method combined with the YOLOv8 network is proposed to improve the EMT reconstructed image quality. Compared with traditional fixed-sensitivity-field EMT reconstruction methods, the correlation coefficient increased by an average of 3.8%. Then, a Gaussian second-order derivative super-resolution method is introduced, which uses a Gaussian second-order derivative function to fit fuzzy kernel parameters and deconvolves the degradation matrices of ECT and EMT images, solving the edge-blurring problem in their fusion. Compared to traditional deblurring methods such as Wiener filtering and single Gaussian kernel modelling, PSNR improved by an average of 1.4 dB, while SSIM improved by an average of 0.03. Finally, an image fusion method based on Latent Low-Rank Representation is proposed by extracting global and local feature information from super-resolution preprocessed ECT and EMT grayscale matrices, respectively. Compared to mainstream pixel-level fusion methods such as wavelet transforms, it resolves the issue of performance degradation in traditional fusion methods when observational data is insufficient. All validations were conducted using simulation data from COMSOL, with sensitivity field optimisation adapted to the complex flow patterns of three-phase flow scenarios. The super-resolution module addressed reconstructed images exhibiting low signal-to-noise ratios and blurred edges, whilst the LatLRR fusion method maintained stable fusion performance even under small-sample conditions.