AIMC Topic: Signal-To-Noise Ratio

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[A heart sound classification method based on complete ensemble empirical modal decomposition with adaptive noise permutation entropy and support vector machine].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Heart sound signal is a kind of physiological signal with nonlinear and nonstationary features. In order to improve the accuracy and efficiency of the phonocardiogram (PCG) classification, a new method was proposed by means of support vector machine ...

Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer.

Bioinformatics (Oxford, England)
MOTIVATION: Cryo-electron microscopy (cryo-EM) is a widely used technology for ultrastructure determination, which constructs the 3D structures of protein and macromolecular complex from a set of 2D micrographs. However, limited by the electron beam ...

DENOISING SWEPT SOURCE OPTICAL COHERENCE TOMOGRAPHY VOLUMETRIC SCANS USING A DEEP LEARNING MODEL.

Retina (Philadelphia, Pa.)
PURPOSE: To evaluate the use of a deep learning noise reduction model on swept source optical coherence tomography volumetric scans.

Low-dose CT noise reduction based on local total variation and improved wavelet residual CNN.

Journal of X-ray science and technology
BACKGROUND: Low-dose computed tomography (LDCT) is an effective method for reducing radiation exposure. However, reducing radiation dose leads to considerable noise in the reconstructed image that can affect doctor's judgment.

Accurate OSNR monitoring based on data-augmentation-assisted DNN with a small-scale dataset.

Optics letters
Deep neural networks (DNNs) have been successfully applied for accurate optical signal-to-noise ratio (OSNR) monitoring. However, the performance of OSNR monitoring substantially degrades when a mega dataset is inaccessible. Here, we demonstrate an a...

Novel U-net based deep neural networks for transmission tomography.

Journal of X-ray science and technology
BACKGROUND: The fusion of computer tomography and deep learning is an effective way of achieving improved image quality and artifact reduction in reconstructed images.

Reducing speckle in anterior segment optical coherence tomography images based on a convolutional neural network.

Applied optics
Speckle noise is ubiquitous in the optical coherence tomography (OCT) image of the anterior segment, which greatly affects the image quality and destroys the relevant structural information. In order to reduce the influence of speckle noise in OCT im...

DeepSinse: deep learning-based detection of single molecules.

Bioinformatics (Oxford, England)
MOTIVATION: Imaging single molecules has emerged as a powerful characterization tool in the biological sciences. The detection of these under various noise conditions requires the use of algorithms that are dependent on the end-user inputting several...

Forward Model and Deep Learning Based Iterative Deconvolution for Robust Dynamic CT Perfusion.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Perfusion maps obtained from low-dose computed tomography (CT) data suffer from poor signal to noise ratio. To enhance the quality of the perfusion maps, several works rely on denoising the low-dose CT (LD-CT) images followed by conventional regulari...