An Electrical Impedance Tomography Method under Noise

The inverse problem solution in electrical impedance tomography (EIT) is sensitive to measurement noise. Due to modeling errors and contact impedance, EIT measurement data are often contaminated by noise, which affects the accuracy of image reconstruction. This study introduces a novel denoising method combining discrete wavelet transform (DWT) with a denoising autoencoder (DAE) to effectively address measurement noise. The DWT–DAE method was systematically evaluated on various EIT electrode pairs data, including simulated data with different SNR levels (20–60[Formula: see text]dB), water tank measurement data and KIT4 public dataset. Results show that the DWT–DAE method significantly reduces noise in EIT measurement data, improving the SNR from approximately 20–30[Formula: see text]dB to over 55[Formula: see text]dB, thereby enhancing the quality of reconstructed images. To validate the practical efficacy of our approach, we applied it to the challenging case of intact maize ears. The DWT–DAE method was used to denoise the EIT measurements, followed by image reconstruction using the Convex–Concave Procedure-based Block Sparse Bayesian Learning Algorithm (CCPBSBL). Results indicate that this integrated methodology substantially enhances the accuracy of reconstructed conductivity distributions in maize ears, demonstrating its practical value for agricultural applications.