Transmission electron microscopy (TEM) is a microscopy technique in which a beam of electrons is transmitted through a specimen to form an image. The specimen is most often an ultrathin section less than 100 nm thick or a suspension on a grid.

Specifically, a novel signal-to-image transformation method is developed first to preserve the structural features of TEM signals. Then, a novel deep CNN-based denoiser is proposed to further perform feature learning, in which the residual learning mechanism is adopted to model the noise estimation image for different signal features.

Transmission electron microscopy (TEM) is an analytical technique used to visualize the smallest structures in matter. Unlike optical microscopes, which rely on light in the visible spectrum, TEM can reveal stunning detail at the atomic scale by magnifying nanometer structures up …

2025年2月28日 · Transient electromagnetic (TEM) data are often susceptible to various types of noise during field acquisition, which can compromise the reliability of subsurface imaging and interpretation. Here, we propose a novel deep learning-based denoising method called PC …

2021年11月1日 · Transient electromagnetic method (TEM) is often disturbed by surrounding noise in field measurement, which leads to low resolution of data and affects accurate positioning of geological anomalies. In order to solve this problem, an improved variational mode decomposition (VMD) algorithm based on whale optimization algorithm (WOA) is proposed.

In this study, we propose a new TEM signal denoising model WE-TEM1Dformer and a smoothness constraints evaluation method for TEM signals. The local attention mechanism is applied to the TEM signal denoising task, and the denoising capability of the model in uniform half-space TEM signal scenarios is further enhanced by combining local attention ...

2024年4月18日 · Transient electromagnetic (TEM) methods, which are alternatively known as time-domain electromagnetics (TDEM), detect variations in subsurface electrical resistivity by using a few basic electromagnetic (EM) principles.

2025年1月13日 · The Deep-TEMNet model ultimately outputs a rapid and precise simulation of the TEM signal, significantly reducing computation time compared to traditional methods such as finite-element modeling. Deep-TEMNet’s integration of U-Net for spatial encoding with 2D LSTM for temporal modeling presents a highly effective solution for forward modeling ...

2025年1月13日 · In this study, we used the opposing coil TEM method to perform two vertical flips of the measurement coil at the same measurement point, processing the voltage signal difference to eliminate the inductive signal between the multi-turn small-loop coils.

Transmission electron microscopy (TEM) is a powerful tool for imaging material structure and characterizing material chemistry. Recent advances in data collection technology for TEM have enabled high-volume and high-resolution data collection at a microsecond frame rate.