1227 results about "Noise removal" patented technology

A method and system is invented for automated continuous monitoring and real-time analysis of body sounds. The system embodies a multi-sensor data acquisition system to measure body sounds continuously. The sound signal processing functions utilize a unique signal separation and noise removal methodology by which authentic body sounds can be extracted from cross-talk signals and in noisy environments, even when signals and noises may have similar frequency components or statistically dependent. This method and system combines traditional noise canceling methods with the unique advantages of rhythmic features in body sounds. By employing a multi-sensor system, the method and system perform cyclic system reconfiguration, time-shared blind identification and adaptive noise cancellation with recursion from cycle to cycle. Since no frequency separation or signal/noise independence is required, this invention can provide a robust and reliable capability of noise reduction, complementing the traditional methods. The invention further includes a novel method by which pattern recognition of groups of key parameters can be used to diagnosis physical conditions associated with body sounds, with confidence intervals on the diagnostic criterion to indicate accuracy of diagnosis.

The present invention is a method for processing seismic data comprising acquiring seismic data using a sweep sequence including a plurality of cascaded sweep segments. The seismic data include a plurality of data segments and a listen time. The listen is combined with an initial data segment. The seismic data segments are phase shifted to a phase of a target data segment to be denoised (i.e. removal of ambient, harmonic and coherent noise). A difference between the target data segment and the remaining data segments is determined. If the determined difference exceeds a predetermined threshold, data in the target data segment is replaced with data derived from the remaining data segments. The data segments may be stacked to form a new target data segment. The listen time is extracted from the initial data segment.

A method and apparatus to remove color noise included in raw data while effectively preventing image quality degradation. For Interest pixels serially set onto a mosaic image formed of raw data, conversion is executed into a pixel value for noise removal based on a processing reference pixel value having a unified color signal component in each interest pixel, noise is removed from the pixel value for noise removal, and the pixel value for noise removal with noise removed is converted into the source pixel value, whereby only color noise can be removed without affecting a luminance signal.

A method and apparatus is disclosed herein for using an in-the-loop denoising filter for quantization noise removal for video compression. In one embodiment, the video encoder comprises a transform coder to apply a transform to a residual frame representing a difference between a current frame and a first prediction, the transform coder outputting a coded differential frame as an output of the video encoder; a transform decoder to generate a reconstructed residual frame in response to the coded differential frame; a first adder to create a reconstructed frame by adding the reconstructed residual frame to the first prediction; a non-linear denoising filter to filter the reconstructed frame by deriving expectations and performing denoising operations based on the expectations; and a prediction module to generate predictions, including the first prediction, based on previously decoded frames.

A noise suppression apparatus of the present invention includes a voice/non-voice discriminator for discriminating a frame signal divided into frames having a predetermined length; a Fourier transform unit for converting a frame signal into a spectrum; a noise spectrum estimation unit for estimating a noise spectrum of a frame judged as a non-voice signal; an amplitude spectrum subtractor for subtracting the product of an estimated noise spectrum and a predetermined coefficient from a spectrum obtained by the transform unit; an auditory correction noise adder for adding aa auditory correction noise spectrum to a spectrum outputted from the subtractor; and an inverse Fourier transform unit for performing inverse Fourier transform to an output of the adder. The noise suppression apparatus further includes a negative amplitude value counter for counting the number of frequency components in an output of the subtractor whose amplitude values are negative; a subtraction coefficient setting unit for gradually decreasing a subtraction coefficient unit the counted value becomes not more than a predetermined value; an inverse Fourier transform unit for performing inverse Fourier transform to an output of the counter; and a noise spectrum estimation unit for calculating spectrum information of noise in the frame signal using different spectrum information according to the current type of frame signal.

A speech enhancement system improves the perceptual quality of a processed voice signal. The system improves the perceptual quality of a voice signal by removing unwanted noise components from a voice signal. The system removes undesirable signals that may result in the loss of information. The system receives and analyzes signals to determine whether an undesired random or persistent signal corresponds to one or more modeled noises. When one or more noise components are detected, the noise components are substantially removed or dampened from the signal to provide a less noisy voice signal.

The invention discloses a way to recover separated seismograms with reduced interference noise by processing vibroseis data recorded (or computer simulated) with multiple vibrators shaking simultaneously or nearly simultaneously (200). A preliminary estimate of the separated seismograms is used to obtain improved seismograms (201). The preliminary estimate is convolved with the vibrator signature and then used to update the seismogram. Primary criteria for performing the update include fitting the field data and satisfying typical criteria of noise-free seismograms (202). Alternative ways to update are disclosed, including signal extraction, modeled noise extraction, constrained optimization based separation, and penalized least-squares based separation. The method is particularly suited for removing noise caused by separating the combined record into separate records for each vibrator, and is advantageous where the number of sweeps is fewer than the number of vibrators (200).

A robust method of creating process models for use in controller generation, such as in MPC controller generation, adds noise to the process data collected and used in the model generation process. In particular, a robust method of creating a parametric process model first collects process outputs based on known test input signals or sequences, adds random noise to the collected process data and then uses a standard or known technique to determine a process model from the collected process data. Unlike existing techniques for noise removal that focus on clean up of non-random noise prior to generating a process model, the addition of random, zero-mean noise to the process data enables, in many cases, the generation of an acceptable parametric process model in situations where no process model parameter convergence was otherwise obtained. Additionally, process models created using this technique generally have wider confidence intervals, therefore providing a model that works adequately in many process situations without needing to manually or graphically change the model.

A method for noise removal from color barcode images includes acquiring a barcode image using a color imaging array and separating the barcode image into color channels. Weighting factors are associated with the color channels and at least one weighting factor is reduced. The weighting factors are applied to the color channels to produce a first transformed image. A portion of the first transformed image is analyzed to produce a first set of decoded data from the barcode.

Images of a subsurface region may be generated in conjunction with reverse time migration with reduced or no backscattering noise. Two or more wavefields may be decomposed to produce two or more corresponding decomposed wavefields. The two or more decomposed wavefields may include a source wavefield and a receiver wavefield. Directivity of the two or more decomposed wavefields may be determined to produce corresponding direction-dependent components of the two or more decomposed wavefields. One or more of the direction-dependent components of one or more decomposed source wavefields may be cross-correlated with one or more of the direction-dependent components of one or more corresponding decomposed receiver wavefields. An image of the subsurface region may be generated based on the cross-correlation.

The invention discloses an action detecting and identifying method based on radio signals and relates to the field of radio network and pervasive computing, in particular to an action detecting and identifying method based on radio signals. Radio signals are interfered by means of human actions, a universal radio device acquires radio signal data, and data is subjected to noise removal and characteristic extraction correlated with action speed to identify action. The radio signal data is acquired via one or more universal radio devices, radio data is subjected to noise removal by means of multichannel radio signal data, and characteristics correlated to human action speed can be extracted from the radio data so as to detect and identify the actions. The action detecting and identifying method includes steps of data acquisition, data denoising, data segmenting, characteristic extraction, model training and action identifying.

An image processing apparatus includes an image correction processing unit configured to correct an input image so as to generate a corrected image and a super-resolution processing unit configured to receive the corrected image generated by the image correction processing unit and increase a resolution of the corrected image through super-resolution processing so as to generate a high-resolution image. The image correction processing unit performs at least one of a time-direction noise removal process, a space-direction noise removal process, a compression noise removal process, and an aperture control correction process.

Reduction of noise in a device having a console with one or more microphones and a source of narrow band distributed noise located on the console is disclosed. A microphone signal containing a broad band distributed desired sound and narrow band distributed noise is divided amongst a plurality of frequency bins. For each frequency bin, it is determined whether a portion of the signal within the frequency bin belongs to a narrow band distribution characteristic of the source of narrow band noise located on the console. Any frequency bins containing portions of the signal belonging to the narrow band distribution are filtered to reduce the narrow band noise.

The present invention relates to an image processing device and method enabling noise removal to be performed according to images and bit rates.

A low-pass filter setting unit 93 sets, from filter coefficients stored in a built-in filter coefficient memory 94, a filter coefficient corresponding to intra prediction mode information and a quantization parameter. A neighboring image setting unit 81 uses the filter coefficient set by the low-pass filter setting unit 93 to subject neighboring pixel values of a current block from frame memory 72 to filtering processing. A prediction image generating unit 82 performs intra prediction using the neighboring pixel values subjected to filtering processing, from the neighboring image setting unit 81, and generates a prediction image. The present invention can be applied to an image encoding device which encodes with the H.264/AVC format, for example.

An image signal processor for processing an input image signal to output an output image signal includes an input unit receiving the input image signal, a noise removal unit removing noise from the input image signal, and a scaler reducing, maintaining or magnifying the image size of the input image signal. The scaler directly receives the input image signal from the input unit or the noise removal unit in response to a route control signal, reduces the image size of the input image signal when the image size of the input image signal is larger than the image size of the output image signal and stores the image signal with a reduced image size in a memory. The scaler maintains or magnifies the image size of the image signal stored in the memory and outputs the image signal with a maintained or magnified image size as the output image signal.

In the noise suppression apparatus, a spectrum correction gain calculation unit calculates the noise amplitude spectrum correction gain and the noise removal spectrum correction gain using the input amplitude spectrum, noise amplitude spectrum and respective coefficients; a spectrum deduction unit deducts the product of the noise amplitude spectrum and the noise amplitude spectrum correction gain from the input amplitude spectrum and outputs the result as a first noise removal spectrum; a spectrum suppression unit multiplies the first noise removal spectrum by the noise removal spectrum correction gain and outputs the result as a second noise removal spectrum; finally a frequency/time conversion unit converts the second noise removal spectrum into a time domain signal.

The invention discloses a multi-frequency weak signal detection method for detecting the failure of a rotor in the early stage. A related method is adopted to conduct prophase noise removal, and a chaotic oscillator array scanning method is adopted to realize simultaneous detection of a plurality of frequency weak signals represented by the same failure, that is, signals with noise are denoised through related calculation, a related function is sent to the chaotic oscillator array and serves as a part of chaotic system periodic driving force, and the weak periodic signal of a signal to be detected can be detected through the change of chaotic system phase locus. The method improves the signal-to-noise ratio of the signal to be detected, and completes the detection of multi-feature weak signal through identifying the state of Duffing oscillator, namely the change of phase locus pattern. Therefore, the method has the advantages of simplicity, intuition, definite physical significance and high detection signal-to-noise ratio. Furthermore, the method is capable of completing the weak signal detection of a plurality of known frequencies and easy for engineering realization, thereby having greater development potential.

Enhanced removing of noise and outliers from one or more point sets generated by image-based 3D reconstruction techniques is provided. In accordance with the disclosure, input images and corresponding depth maps can be used to remove pixels that are geometrically and/or photometrically inconsistent with the colored surface implied by the input images. This allows standard surface reconstruction methods (such as Poisson surface reconstruction) to perform less smoothing and thus achieve higher quality surfaces with more features. In some implementations, the enhanced point-cloud noise removal in accordance with the disclosure can include computing per-view depth maps, and detecting and removing noisy points and outliers from each per-view point cloud by checking if points are consistent with the surface implied by the other input views.

There is provided a pulse wave data analyzing method for extracting vital information out of pulse wave data concerning a living body. The method comprises a noise removal step of: detecting bottom values and peak values along a time axis in a time-series manner out of pulse wave data obtained by sequentially measuring a pulse wave of a subject for a predetermined period; making pairs with respect to the bottom values and the peak values adjacent to each other on the time axis to obtain bottom-to-peak amplitude values along the time axis, the bottom-to-peak amplitude value being a difference between the bottom value and the peak value in each of the pairs; and comparing each set of the two bottom-to-peak amplitude values adjacent to each other along the time axis to remove the bottom value and the peak value relating to the smaller bottom-to-peak amplitude value in the each set as a noise, if a ratio of the one of the two bottom-to-peak amplitude values to the other one of the two bottom-to-peak amplitude values is larger than a predetermined value.

An apparatus for removing a noise of a video signal is disclosed, by which a noise level can be effectively estimated to enhance an image quality of the video signal, by which the noise can be removed in a manner of effectively estimating a noise level though motion adaptive filtering, and by which blurring is prevented in the process of removing the noises. The present invention includes a temporal noise level estimation unit estimating a level of a temporal noise included in the video signal using a difference between two temporally consecutive videos, a noise correction unit correcting a noise estimated by the temporal noise level estimation unit, and a noise removal unit removing the noise included in the video signal using a level of the corrected noise.