Under the condition of low signal-to-noise ratio, the target detection performance of radar decreases, which seriously affects the tracking and recognition for the long-range small targets. To solve it, this paper proposes a target detection algorith...
Deep neural networks have shown great improvements in low-dose computed tomography (CT) denoising. Early algorithms were primarily optimized to obtain an accurate image with low distortion between the denoised image and reference full-dose image at t...
A point contact/Coulomb coupling technique is generally used for visualizing the ultrasonic waves in Lead Zirconate Titanate (PZT) ceramics. The point contact and delta pulse excitation produce a broadband frequency spectrum and wide directional wave...
Volumetric functional imaging is widely used for recording neuron activities in vivo, but there exist tradeoffs between the quality of the extracted calcium traces, imaging speed, and laser power. While deep-learning methods have recently been applie...
Automatic modulation discrimination (AMC) is one of the critical technologies in spatial cognitive communication systems. Building a high-performance AMC model in intelligent receivers can help to realize adaptive signal synchronization and demodulat...
OBJECTIVES: To investigate a deep learning reconstruction algorithm to reduce the time of synthetic MRI (SynMRI) scanning on the breast and improve the image quality.
The use of multi-connectivity has become a useful tool to manage the traffic in heterogeneous cellular network deployments, since it allows a device to be simultaneously connected to multiple cells. The proper exploitation of this technique requires ...
PURPOSE: Recently, deep learning-based methods have been established to denoise the low-count positron emission tomography (PET) images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan tim...
IEEE journal of biomedical and health informatics
Aug 11, 2022
Medical image denoising faces great challenges. Although deep learning methods have shown great potential, their efficiency is severely affected by millions of trainable parameters. The non-linearity of neural networks also makes them difficult to be...