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Signal-To-Noise Ratio

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Towards Image Guided Magnetic Resonance Elastography via Active Driver Positioning Robot.

IEEE transactions on bio-medical engineering
UNLABELLED: Magnetic Resonance Elastography (MRE) is a developing imaging technique that enables non-invasive estimation of tissue mechanical properties through the combination of induced mechanical displacements in the tissue and Magnetic Resonance ...

Deblurring of Sound Source Orientation Recognition Based on Deep Neural Network.

Sensors (Basel, Switzerland)
Underwater target detection and identification technology are currently two of the most important research directions in the information disciplines. Traditionally, underwater target detection technology has struggled to meet the needs of current eng...

Low SNR Multi-Emitter Signal Sorting and Recognition Method Based on Low-Order Cyclic Statistics CWD Time-Frequency Images and the YOLOv5 Deep Learning Model.

Sensors (Basel, Switzerland)
It is difficult for traditional signal-recognition methods to effectively classify and identify multiple emitter signals in a low SNR environment. This paper proposes a multi-emitter signal-feature-sorting and recognition method based on low-order cy...

Efficient learning representation of noise-reduced foam effects with convolutional denoising networks.

PloS one
This study proposes a neural network framework for modeling the foam effects found in liquid simulation without noise. The position and advection of the foam particles are calculated using the existing screen projection method, and the noise problem ...

Ultrafast lumbar spine MRI protocol using deep learning-based reconstruction: diagnostic equivalence to a conventional protocol.

Skeletal radiology
OBJECTIVE: To evaluate the diagnostic equivalency between an ultrafast (1 min 53 s) lumbar MRI protocol using deep learning-based reconstruction and a conventional lumbar MRI protocol (12 min 31 s).

Comparison of CT noise reduction performances with deep learning-based, conventional, and combined denoising algorithms.

Medical engineering & physics
Conventional noise reduction algorithms have been used in image processing for a very long time, but recently, deep learning-based algorithms have been shown to significantly reduce the noise in CT images. In this paper, a comparison of CT noise redu...

Denoising diffusion weighted imaging data using convolutional neural networks.

PloS one
Diffusion weighted imaging (DWI) with multiple, high b-values is critical for extracting tissue microstructure measurements; however, high b-value DWI images contain high noise levels that can overwhelm the signal of interest and bias microstructural...

A densely interconnected network for deep learning accelerated MRI.

Magma (New York, N.Y.)
OBJECTIVE: To improve accelerated MRI reconstruction through a densely connected cascading deep learning reconstruction framework.

Wasserstein Adversarial Regularization for Learning With Label Noise.

IEEE transactions on pattern analysis and machine intelligence
Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning robust classifiers in presence of noisy data. To achieve this goal, we p...

Boundary-Preserved Deep Denoising of Stochastic Resonance Enhanced Multiphoton Images.

IEEE journal of translational engineering in health and medicine
OBJECTIVE: With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conv...