AIMC Topic: Signal-To-Noise Ratio

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Two-Stage CNN Model for Joint Demosaicing and Denoising of Burst Bayer Images.

Computational intelligence and neuroscience
In the classical image processing pipeline, demosaicing and denoising are separated steps that may interfere with each other. Joint demosaicing and denoising utilizes the shared image prior information to guide the image recovery process. It is expec...

State-of-the-Art Capability of Convolutional Neural Networks to Distinguish the Signal in the Ionosphere.

Sensors (Basel, Switzerland)
Recovering and distinguishing different ionospheric layers and signals usually requires slow and complicated procedures. In this work, we construct and train five convolutional neural network (CNN) models: DeepLab, fully convolutional DenseNet24 (FC-...

AI Denoising Significantly Enhances Image Quality and Diagnostic Confidence in Interventional Cone-Beam Computed Tomography.

Tomography (Ann Arbor, Mich.)
(1) To investigate whether interventional cone-beam computed tomography (cbCT) could benefit from AI denoising, particularly with respect to patient body mass index (BMI); (2) From 1 January 2016 to 1 January 2022, 100 patients with liver-directed in...

DeBoNet: A deep bone suppression model ensemble to improve disease detection in chest radiographs.

PloS one
Automatic detection of some pulmonary abnormalities using chest X-rays may be impacted adversely due to obscuring by bony structures like the ribs and the clavicles. Automated bone suppression methods would increase soft tissue visibility and enhance...

Deep-learning two-photon fiberscopy for video-rate brain imaging in freely-behaving mice.

Nature communications
Scanning two-photon (2P) fiberscopes (also termed endomicroscopes) have the potential to transform our understanding of how discrete neural activity patterns result in distinct behaviors, as they are capable of high resolution, sub cellular imaging y...

Parametric image generation with the uEXPLORER total-body PET/CT system through deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: Total-body dynamic positron emission tomography/computed tomography (PET/CT) provides much sensitivity for clinical imaging and research, bringing new opportunities and challenges regarding the generation of total-body parametric images. Thi...

Maskless 2-Dimensional Digital Subtraction Angiography Generation Model for Abdominal Vasculature using Deep Learning.

Journal of vascular and interventional radiology : JVIR
PURPOSE: To develop a deep learning (DL) model to generate synthetic, 2-dimensional subtraction angiograms free of artifacts from native abdominal angiograms.

Classification of breast cancer with deep learning from noisy images using wavelet transform.

Biomedizinische Technik. Biomedical engineering
In this study, breast cancer classification as benign or malignant was made using images obtained by histopathological procedures, one of the medical imaging techniques. First of all, different noise types and several intensities were added to the im...

Reconstructing high fidelity digital rock images using deep convolutional neural networks.

Scientific reports
Imaging methods have broad applications in geosciences. Scanning electron microscopy (SEM) and micro-CT scanning have been applied for studying various geological problems. Despite significant advances in imaging capabilities, and image processing al...