AIMC Topic: Signal-To-Noise Ratio

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An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation.

IEEE journal of biomedical and health informatics
High Resolution (HR) medical images provide rich anatomical structure details to facilitate early and accurate diagnosis. In magnetic resonance imaging (MRI), restricted by hardware capacity, scan time, and patient cooperation ability, isotropic 3-di...

Dual-domain fusion deep convolutional neural network for low-dose CT denoising.

Journal of X-ray science and technology
BACKGROUND: In view of the underlying health risks posed by X-ray radiation, the main goal of the present research is to achieve high-quality CT images at the same time as reducing x-ray radiation. In recent years, convolutional neural network (CNN) ...

FNSAM: Image super-resolution using a feedback network with self-attention mechanism.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: High-resolution (HR) magnetic resonance imaging (MRI) provides rich pathological information which is of great significance in diagnosis and treatment of brain lesions. However, obtaining HR brain MRI images comes at the cost of extending...

Edge feature extraction-based dual CNN for LDCT denoising.

Journal of the Optical Society of America. A, Optics, image science, and vision
In low-dose computed tomography (LDCT) denoising tasks, it is often difficult to balance edge/detail preservation and noise/artifact reduction. To solve this problem, we propose a dual convolutional neural network (CNN) based on edge feature extracti...

Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging.

Journal of biomedical optics
SIGNIFICANCE: Quantitative optoacoustic imaging (QOAI) continues to be a challenge due to the influence of nonlinear optical fluence distribution, which distorts the optoacoustic image representation. Nonlinear optical fluence correction in OA imagin...

Uneven wrapped phase pattern denoising using a deep neural network.

Applied optics
The wrapped phase patterns obtained from an object composed of different materials have uneven gray values. In this paper, we improve the dilated-blocks-based deep convolution neural network (DBDNet) and build a new dataset for restoring the uneven g...

Statistical neural network (SNN) for predicting signal-to-noise ratio (SNR) from static parameters and its validation in 16-bit, 125-MSPS analog-to-digital converters (ADCs).

The Review of scientific instruments
In the analog-to-digital converter (ADC) test process, the static and dynamic performance parameters are the most important, and the tests for these parameters account for the bulk of the ADC test cost. These two types of parameters follow certain re...

Attention-based neural network for polarimetric image denoising.

Optics letters
In this Letter, we propose an attention-based neural network specially designed for the challenging task of polarimetric image denoising. In particular, the channel attention mechanism is used to effectively extract the features underlying the polari...

Framework for denoising Monte Carlo photon transport simulations using deep learning.

Journal of biomedical optics
SIGNIFICANCE: The Monte Carlo (MC) method is widely used as the gold-standard for modeling light propagation inside turbid media, such as human tissues, but combating its inherent stochastic noise requires one to simulate a large number photons, resu...