The denoising of mechanical system is always an indispensable process in sensor signal
analysis. It directly affects the result of subsequent tool state monitoring and identification …

With the fast development of deep learning models, hierarchical convolutional neural
networks have achieved great success in image denoising tasks. To further boost the …

Recently, with the broad applications of deep learning technology in image denoising, many
deep neural networks based on the residual block (ResBlock) have been proposed to …

Suppressing random noise in seismic data is a significant problem in seismic data
processing. Often, there is serious aliasing between the effective signal and random noise …

Wetland experimental images are often affected by factors such as waves, weather
conditions, and lighting, resulting in severe noise in the images. In order to improve the …

Recently, convolutional neural networks (CNNs) and attention mechanisms have been
widely used in image denoising and achieved satisfactory performance. However, the …

Recently, self-supervised denoising methods have attracted significant attention due to the
considerable challenge posed by constructing a large-scale real noise dataset for …

Purpose 23 Na MRI can be used to quantify in-vivo tissue sodium concentration (TSC), but
the inherently low 23 Na signal leads to long scan times and/or noisy or low-resolution …

摘要颜料的分类识别是古代壁画进行保护修复的基础, 多光谱成像方法能够无损快速地获取壁画
颜料的光谱图像数据并进行分析. 传统利用卷积神经网络进行特征提取的算法中连续的卷积和池 …

Hyperspectral imaging critically serves for various fields such as remote sensing, biomedical
and agriculture. Its potential can be exploited to a greater extent when combined with deep …