69 results about "Noise Artifact" patented technology

The invention discloses a low-dose CT image processing system based on a noise artifact suppression convolutional neural network. The system includes: an image preprocessing module, which is used to obtain multiple sets of matched low-dose CT images V<s><ld>and conventional dose CT images V<s><rd> and to subtract V<s><ld> and V<s><ld> to obtain noise artifact image Ns; a noise artifact suppression convolutional neural network building module, which is used for using V<s><ld> as a training image and Ns as a label image to establish a mapped convolutional neural network between V<s><ld> and Ns;a network training module, configured to train a noise artifact suppression convolutional neural network by reducing a loss function of the neural network; a network processing module, which is usedfor inputting a low-dose CT image to be processed V<t><ld> to the mapping convolutional neural network for processing and obtaining a predicted noise artifact image N<t>

; and a noise artifact suppression module, which is used for subtracting N<t>

from V<t><ld> to obtain a noise artifact suppressed image V<t>

. The present invention can effectively suppress noise in low dose CT data Acoustic artifacts, the image quality after processing can meet the clinical analysis, diagnosis and other requirements, and the image effect of low-dose CT imaging is improved.

Methods and systems for quantitatively detecting the presence of artifacts in physiological measurement data and for determining usable data among those that have been designated to be corrupted with artifacts are presented.

A modular AFM/SPM which provides faster measurements, in part through the use of smaller probes, of smaller forces and movements, free of noise artifacts, that the old generations of these devices have increasingly been unable to provide. The modular AFM/SPM includes a chassis, the foundation on which the modules of the instrument are supported; a view module providing the optics for viewing the sample and the probe; a head module providing the components for the optical lever arrangement and for steering and focusing those components; a scanner module providing the XYZ translation stage that actuates the sample in those dimensions and the engage mechanism; a isolation module that encloses the chassis and provides acoustic and/or thermal isolation for the instrument and an electronics module which, together with the separate controller, provide the electronics for acquiring and processing images and controlling the other functions of the instrument. All these modules and many of their subassemblies are replaceable and potentially upgradeable. This allows updating to new technology as it becomes available.

In a video system, a method and system for block noise reduction are provided. Edge parameters based on spatial variance may be determined to detect vertical edges that may result from block noise. These edge parameters may be determined serially. Once detected, pixels neighboring the vertical edges may be filtered and clamped to determine a vertical block noise reduction difference (BNR) parameter. Similarly, edge parameters based on spatial variance may be determined to detect horizontal edges that may result from block noise. These edge parameters may be determined serially. Once detected, pixels neighboring the horizontal edge may be filtered and clamped to determine a horizontal BNR difference parameter. The vertical and horizontal BNR difference parameters may be utilized to reduce block noise artifacts in the video image.

The scene is illuminated by light pulses at higher intensities at predetermined times than at times between the predetermined times. Images of the scene are captured at the predetermined times to provide a sequence of anchor frames in a stream of images, and images of the scene are captured at times between said predetermined times to provide non-anchor frames in the stream. The anchor and non-anchor frames are then compressed and processed to produce video with greater compression and reduced noise artifacts.

The scene is illuminated by light pulses with wavelengths outside the visible range. Upon sensing motion, the scene is illuminated by light pulses to provide video in the visible wavelength range.

The scene is illuminated by a sequence of light pulses of different wavelengths when motion is detected in the scene. Different wavelength images of the scene are combined to provide higher resolution colored images without color filtering.

In a video system, a method and system for mosquito noise reduction are provided. Mosquito noise may be detected by determining a block variance parameter for an image block and a local variance parameter for a portion of the image block. The block variance parameter may be based on serially determined horizontal and vertical variance parameters. A clamping limit is also determined based on the block variance parameter, the local variance parameter, a relative weight parameter, and a mosquito core limit parameter. Pixels covered by the local variance may be filtered and the filtered output may be compared to the original pixel values to determine a difference parameter. Different filter values may be utilized for progressive and interlaced video. A mosquito noise reduction (MNR) difference parameter may be determined based on the difference parameter and the clamping limit and may be utilized to reduce mosquito noise artifacts.

The invention discloses a convolutional neural network-based low-dose CT image decomposition method and belongs to the X-ray computed tomography technical field. The method of the invention includes the following steps that: step 1, training images including low-dose CT images V<ld>s and low-dose CT images V<rd>s are reconstructed, and subtraction operation is performed on the low-dose CT images V<ld>s and low-dose CT images V<rd>s, so that noise artifact images Ns can be obtained, wherein Ns, V<ld>s and V<rd>s satisfy an equation that Ns=V<ld>s-V<rd>s; step 2, a mapping convolutional neural network between the low-dose CT images V<ld>s and the noise artifact image Ns is constructed; step 3, a certain quantity of low-dose CT images V<ld>s and the corresponding noise artifact image Ns are adopted to train the constructed convolutional neural network; and step 4, the trained convolutional neural network is adopted to process selected low-dose CT images V<ld>s, so that the decomposition of anatomical structure components and noise artifact structure components in the selected low-dose CT images V<ld>s can be realized. With the method provided by the invention adopted, star-shaped artifact noises and structural features in low-dose CT images can be efficiently separated from each other.

A method and apparatus are provided for processing a received digital radio broadcast signal to efficiently remove signal interference artifacts from digital and/or analog signals by using signal quality information extracted from audio samples in one or more buffered audio frames to detect audio frames containing clipped noise artifacts and weaker noise artifacts and to selectively apply anti-interference processing to remove the signal interference artifacts.

A system and method of identifying and removing artifact from radio frequency noise from biopotentials identifies epochs contaminated with radio frequency noise. Contaminated epochs are then replaced with recent uncontaminated epochs stored in a buffer, depending on the current level of artifact and the availability of suitable data. Discontinuities arising at the beginning of the replaced epochs are smoothed by means of a windowing function.

Technologies are provided herein for real-time detection of motion and noise (MN) artifacts in electrocardiogram signals recorded by electrocardiography devices. Specifically, the present disclosure provides techniques for increasing the accuracy of identifying paroxysmal atrial fibrillation (AF) rhythms, which are often measured via such devices. According to aspects of the present disclosure, a method for detecting MN artifacts in an electrocardiogram (ECG) recording includes receiving an ECG segment and decomposing the received ECG segment into a sum of intrinsic mode functions. The intrinsic mode functions associated with MN artifacts present within the ECG segment are then isolated. The method further includes determining randomness and variability characteristic values associated with the isolated intrinsic mode functions and comparing the randomness and variability characteristic values to threshold randomness and variability characteristic values. If the randomness and variability characteristic values exceed the threshold characteristic values, the ECG signal is determined to include MN artifacts.

Systems and methods for reducing noise artifacts in an image are disclosed. A radial distance-based look up table may be used to calibrate a noise property change due to the vignette effect. Based on the look up table, a spatial varying noise reduction level is computed to correct the noise property distortion due to the image vignetting.

A biosensor of the invention is a capacitive noncontact sensor with two sensor channels split into a plurality of physically interdigitated symmetrical electrodes and shield sections. Two capacitive plates are electrically connected to the two sensor channels. The capacitive noncontact sensor is sized and packaged to be worn by a person to place the capacitive plates close to the skin of the person and form first and second channel input capacitors with the skin. A signal reconstruction circuit obtains a bio signal from the first and second channel input capacitors through the electrodes by reconstructing differences in the two sensor channels. The circuit includes different parasitic input capacitance in the two channels to create channel-specific outputs that depend on input coupling capacitance.

A pulse oximeter embedded with a novel motion and noise artifact (MNA) detection algorithm based on extraction of time-varying spectral features that are unique to the clean and corrupted components.

A magnetocardiogram (MCG) system with reduced noise artifacts is produced by first creating high-resolution image representations of low-resolution measurements obtained with a magnetic field sensor unit. The high-resolution image representations are created by use of a PCA model that has been trained using a library of ideal, no-noise, high-resolution images. The Biot-Sarvart Law is then used to create a 3D model of a current impulse, given the high-resolution image representations. From the 3D current impulse model, ideal sensor unit measurements as they would have been obtained using a theoretical sensor unit observing the 3D current impulse model are synthesized.

The invention relates to a method for eliminating an EEG signal noise artifact. The method comprises the steps of performing independent component separation on an EEG signal after denoising processing, then selecting an independent component with an artifact, and finally reconstructing the independent component which does not comprise the artifact for obtaining the EEG signal after noise artifactelimination, wherein independent component separation is based on an improved five-order FastICA algorithm. The improvement of the method is characterized in that an improved updating formula is usedfor updating the separated vectors and the relaxation factor of the independent component; and the independent component which comprises the artifact is selected based on a multi-domain adaptive threshold signal selecting method. According to the method for eliminating the EEG signal noise artifact, not only can a plurality of ocular artifacts be automatically identified and eliminated, but alsoa large amount of EEG information can be reserved. Furthermore the method can well improve the signal-to-noise ratio of the EEG signal and reduce the mean square error of the signal.

A modular AFM/SPM which provides faster measurements, in part through the use of smaller probes, of smaller forces and movements, free of noise artifacts, that the old generations of these devices have increasingly been unable to provide. The modular AFM/SPM includes a chassis, the foundation on which the modules of the instrument are supported; a view module providing the optics for viewing the sample and the probe; a head module providing the components for the optical lever arrangement and for steering and focusing those components; a scanner module providing the XYZ translation stage that actuates the sample in those dimensions and the engage mechanism; a isolation module that encloses the chassis and provides acoustic and/or thermal isolation for the instrument and an electronics module which, together with the separate controller, provide the electronics for acquiring and processing images and controlling the other functions of the instrument. All these modules and many of their subassemblies are replaceable and potentially upgradeable. This allows updating to new technology as it becomes available.

The invention discloses an on-line artifact removing method synchronizing EEG-fMRI. The method comprises the following steps: 1) high-pass filtering of EEG signals; 2) upsampling the filtered signal and synchronizing the EEG signal with the marker emitted by the MRI device; 3) obtain a signal Sr with residual artifacts; 4) residual gradient artifacts are present in the signal Sr, and gradient artifacts template denoising and PCA denoising are constructed to obtain the gradient artifacts canceled signal Sc; 5) ECG is contain in that signal Sc, and a QRS peak of the ECG is detected; 6) clustering the ECG with K-means according to different periods; 7) reintegrate each EEG signal into m-segment EEG signals accord to time sequence, and performing OBS processing on that m-segment EEG signals; 8) according to the time sequence of the original signal, reconstructing EEG signal to obtain EEG signal after removing ECG artifact. The invention can collect EEG signal at the same time of scanning and collecting fMRI image, and real-time remove artifact through denoising algorithm to obtain EEG signal with high signal-to-noise ratio.

The present invention involves detecting dark noise artifacts in coded images and video. Locations of artifacts in compressed pictures are found. A strength of the artifact per block is determined as is an overall dark noise artifact strength for each picture. Artifact detection and strength assignment is performed by analyzing candidate areas that could be prone to this type of artifact. Multiple features such as block variance, color information, luminance levels and location of the artifact could be used in this process. Also, median filtering may be used on the identified areas to eliminate isolated areas. A final artifact parameter for each picture can be assessed based on the total number of blocks that are classified as dark noise and also the strength of each macroblock.

The invention belongs to the technical field of CT imaging, and adopts the specific scheme that a multi-scale feature generative adversarial network for suppressing artifact noise in a low-dose CT image is adopted, an LDCT image noise reduction model is selected, an LDCT image and NDCT image data set is constructed, the LDCT image is input into an error feedback pyramid generator network, he pyramid generator network extracts cross-scale features of the LDCT image from different angles, the LDCT image is processed by the error feedback pyramid generator network and then a preliminary noise reduction result graph is output, the NDCT image and the preliminary noise reduction result graph are jointly input into an interleaved convolution discriminator sub-network for iterative training, and afinal noise reduction result graph is output; the error feedback pyramid generator can extract shallow features and deep features in the same scale of the image, increase the richness of feature extraction, improve the discrimination capability of the discriminator, and the multi-scale feature generative adversarial network solves the problem of under-noise reduction or over-noise reduction caused by high similarity between noise artifacts and tissue structure distribution.

A defibrillator and method for using a defibrillator which adopts an ECG analysis algorithm that can detect a cardiac arrhythmia in the presence of noise artifact induced by cardio pulmonary resuscitation (CPR) compressions. The apparatus and method offers guidance throughout a cardiac rescue protocol involving both defibrillation shocks and CPR that improves the effectiveness of the rescue, resulting in more CPR "hands-on" time, better treatment of refibrillation, and reduced transition times between CPR and electrotherapy.