90 results about "Noise statistics" patented technology

A system and process for identifying corresponding points among multiple images of a scene is presented. This involves a multi-view matching framework based on a new class of invariant features. Features are located at Harris corners in scale-space and oriented using a blurred local gradient. This defines a similarity invariant frame in which to sample a feature descriptor. The descriptor actually formed is a bias/gain normalized patch of intensity values. Matching is achieved using a fast nearest neighbor procedure that uses indexing on low frequency Haar wavelet coefficients. A simple 6 parameter model for patch matching is employed, and the noise statistics are analyzed for correct and incorrect matches. This leads to a simple match verification procedure based on a per feature outlier distance.

A method is provided for detecting spot defects on an object when an allowable variation (called the “background”) in the appearance of the object can be modeled. Methods are also provided for measuring and classifying detected spot defects. An alignment model is used to align the image of the object, a background model is then used to estimate the (possibly different) background in each region, and each background is substantially removed from the image so as to form a foreground image on which blob analysis can be applied to detect spot defects, the blob analysis using a threshold image that accommodates different noise statistics for each region. The method facilitates robust spot defect inspection of fiber optic end faces, or of any object with different object regions. The method also allows use of blob analysis over a larger range of conditions, including conditions that make simple blob analysis infeasible.

An apparatus for and method of generating normalized soft decision information output from an inner decoder (i.e. equalizer) in a communications receiver. The invention is operative to normalize the soft decision information before it enters a soft outer decoder. The normalization is performed using a noise power estimate that is dynamically calculated in response to changing noise statistics on the channel. The normalized soft decision output is then applied to the soft outer decoder thus realizing maximum performance therefrom. The noise power estimate is derived from the training sequence and/or the data portion of the received signal. Both types of estimates are calculated. A binary or smoothly weighted average is calculated using both types of estimates. The weighting factor is determined based on one or more performance metrics, such as the Signal to Noise Ratio (SNR).

A method and apparatus for motion blur and ghosting prevention in imaging system is presented. A residue image is computed by performing spatial-temporal filter with a set of absolute image difference of image pairs from input images. A noise adaptive pixel threshold is computed for every pixel based on noise statistics of image sensor. The residue image and the noise adaptive pixel threshold are used to create a motion masking map. The motion masking map is used to represent motion and non-motion pixels in pixels merging. The pixels merging step is performed to generate an output image by considering the motion pixels where the motion pixels are performed separately. The resulting output image having no or less motion blur and ghosting artifacts can be obtained, even the input images having different degree of motion blur between each of the image, while the complexity is low. It is preferred that the current invention is applied in the Bayer raw domain. The benefit is reduced computation and memory because only 1 color component is processed for each pixel. Another benefit is higher signal fidelity because processing in the Bayer raw domain is unaffected by demosaicing artifacts, especially along edges. However, the current invention can also be applied in RGB domain.

A method for synthesizing noise in a digital image comprises the steps of (a) estimating noise statistics based on a set of spatial autocorrelation coefficients and a set of spectral correlation coefficients that correspond to the color channels of the image, where at least one of the spatial autocorrelation coefficients is weighted by at least one of the spectral correlation coefficients, thereby providing a weighted set of autocorrelation coefficients, and where a set of filter coefficients is obtained from the weighted set of autocorrelation coefficients; and (b) synthesizing a synthetic texture by using the filter coefficients to filter a random noise field in each of the color channels, thereby producing output noise fields in each of the color channels that replicate a synthetic texture, e.g., a desired grain appearance, when combined into the digital image.

The invention relates to a self-adaptive volume Kalman filtering method, and in particular relates to a self-adaptive volume Kalman filtering method with a fading memory time-change noise statistic estimator. The method comprises the following steps of: (1) setting initial parameters; (2) updating the time; (3) updating the measurement; (4) constructing the fading memory time-change noise statistics estimator; and (5) estimating and modifying the noise in real time. Compared with a standard volume Kalman filtering method, the method does not demand to make the prior statistic characteristics of the known noise precise and has the self-adaptive capability to copy with the noise change; and moreover, the recursion formula of the noise statistic estimator is simple and easy to realize, and is unbiased to noise statistic estimation.

A global and local statistics controlled noise reduction system in which the video image noise reduction processing is effectively adaptive to both image local structure and global noise level. A noise estimation method provides reliable global noise statistics to the noise reduction system. The noise reduction system dynamically/adaptively configures a local filter for processing each image pixel, and processes the pixel with that local filter. The filtering process of the noise reduction system is controlled by both global and local image statistics that are also computed by the system.

The invention relates to a self-adaptive high-order volume Kalman filtering method in the field of signal processing. The self-adaptive high-order volume Kalman filtering method includes: estimating the one-step predicted target state x (k/k-1) and a covariance matrix P (k/k-1) of the same; calculating fading factor h (k) and using the same to adjust the corrected covariance matrix Po (k/k-1); calculating optimal linear estimation x (k/k) and error covariance P (k/k) of the target state, and estimating the variance Q (k) of system noise in real time. The self-adaptive high-order volume Kalman filtering method has the estimation accuracy higher than that of unscented Kalman filter and volume Kalman filter. In addition, through real-time estimation of the variance of the system noise, filter errors caused by unknown time-varying of noise statistic properties are effectively restricted.

In wireless systems, method and system for weight determination in a single channel (SC) multiple-input multiple-output (MIMO) system for WCDMA/HSDPA are provided. Models of signals received in multiple receive antennas may be determined in a single weight baseband generator (SWBBG) from propagation channel estimates and noise statistics. The models may be utilized to determine combined signal and noise components. The combined signal and noise components may be utilized to determine a plurality of signal-to-noise ratio (SNR) or signal-to-interference-and-noise ratio (SINR) values for various phase and/or amplitude factors. The SINR may be utilized when either single or multiple interfering signals are present. A highest of the SINR or SNR values may be selected to determine a channel weight to apply to the additional receive antennas.

A method for remote crosstalk cancellation, includes: the combination transmitting signal is performed a pre-coding of crosstalk cancellation in the manner of self-adapting filtering, the self-adapting changes of the values of filtering parameters are guided correctly with the influence of the crosstalk component in the received signals which is reflected indirectly by relevant values of the noise statistic reported by the receiver. A corresponding device and a signal processing system for remote crosstalk cancellation are provided. The self-adapting adjustment of the values of filtering parameters has a favorable astringency and an effect for crosstalk cancellation, avoiding the need to know the channel transmission matrix in advance, while absolutely avoiding the conflict to the existing transmission standard.

The invention relates to a high-speed true random quantum number generating method, in particular to a method for producing random quantum number at high speed based on vacuum state quantum fluctuation. The problem that the production speed of an existing vacuum random quantum number generator is lower is solved. According to the specific scheme, the high-speed true random quantum number generating method mainly comprises the following steps that the translation effect of Gaussian distribution is firstly exerted to a vacuum state in a phase space, amplification of vacuum-state orthogonal amplitude component noise in the phase space is achieved, and the entropy content introduced by quantum noise in a system is increased; the true random bits which can be extracted in data postprocessing are increased; the background light of a homodyne detection system is strengthened, the sensitivity of the detection system to vacuum noise is improved, and a vacuum noise measurement value is amplified, so that the framing amount of vacuum noise statistics, namely sampling quantization bits are remarkably increased. By adopting the method, the vacuum random quantum number generating rate is effectively improved, and a new means is provided for manufacturing of high-speed random quantum number generators.

The invention relates to a satellite navigation method for an interactive multi-model UKF with self-adapting factors, and belongs to the field of navigation control. The method comprises the steps that firstly, an auto-covariance matrix of a residual sequence is used as a correcting value, and a smoothing filter is designed for fusing the correcting value with the old self-adapting factor to obtain the new self-adapting factor; secondly, a covariance matrix of process noise is adjusted in real time by utilizing the self-adapting factor, and therefore positioning errors, caused by unknown noise statistics properties, in a system are effectively reduced; afterwards, a model set M is set by the adoption of the interactive multi-model (IMM) algorithm; finally, soft handover between models is achieved by adjusting model probability in real time according to measurement residual errors, so that positioning errors caused by inaccurate system models are reduced. According to the method, the positioning accuracy of a complex motor carrier in a satellite navigation system is effectively improved with models and noise statistical properties unknown.

A novel dual-microphone speech enhancement technique is proposed that utilizes the coherence function between input signals as a criterion for noise reduction. The technique is based on certain assumptions regarding the spatial properties of the target and noise signals and can be applied to arrays with closely spaced microphones, where noise captured by sensors is highly correlated (e.g., inside a mildly reverberant environment). The proposed algorithm is simple to implement and requires no estimation of noise statistics. In addition, it offers the advantage of coping with situations in which multiple interfering sources located at different azimuths might be present.

A method of processing magnetic resonance imaging signals from a plurality of receiver coils of a magnetic resonance imaging system, comprises the steps of receiving from said plurality of receiver coils a corresponding plurality of original signals in the time-domain forming an n-dimensional signal vector ν_k wherein n is the number of receiver coils; linearly combining said original signals so as to obtain a plurality of transformed signals forming an m-dimensional transformed signal vector ν′_k wherein m is smaller than n and wherein said step of linearly combining is represented by a linear transformation matrix A; and reconstructing an image from said plurality of transformed signals. Said transformation matrix A is determined for given sensitivity characteristics and noise statistics of said plurality of receiver coils so as to substantially maximize the signal-to-noise ratio in a preselected image region or volume which is preferably smaller than the imaging slice or volume selected by the magnetic resonance experiment.

In accordance with the present invention, a method and apparatus for estimating the noise and interference over the transmission band for OFDM systems are provided. Noise variance and signal-to-noise ratio (SNR) are important parameters for adaptive orthogonal frequency division multiplexing (OFDM) systems since they serve as a standard measure of signal quality. Conventional algorithms assume that the noise statistics remain constant over the OFDM frequency band, and thereby average the instantaneous noise samples to get a single estimate. In reality, noise is often made up of white Gaussian noise along with correlated colored noise that affects the OFDM spectrum unevenly. Provided is an adaptive windowing technique to estimate the noise power that takes into account the variation of the noise statistics across the OFDM sub-carrier index as well as across OFDM symbols. The proposed method provides many local estimates, allowing tracking of the variation of the noise statistics in frequency and time. A mean-squared-error (MSE) expression in order to choose the optimal window dimensions for averaging in time and frequency is derived.

A machine learning based image processing architecture and associated applications are disclosed herein. In some embodiments, a machine learning framework is trained to learn low level image attributes such as object/scene types, geometries, placements, materials and textures, camera characteristics, lighting characteristics, contrast, noise statistics, etc. Thereafter, the machine learning framework may be employed to detect such attributes in other images and process the images at the attribute level.

The invention discloses a scalar-estimation-algorithm-based INS/GNSS combined navigation method. According to different error dynamic characteristics of a dead reckoning technology and a satellite navigation positioning technology, the two kinds of navigation methods are combined based on a scalar estimation algorithm to construct an INS/GNSS combined navigation system. The method comprises: a discretized INS/GNSS combined navigation system error model is established; on the basis of a kalman filtering algorithm, a scalar estimation algorithm is constructed; on the basis of the scalar estimation algorithm, a new measurement vector Z* is introduced; a state variable of the combined navigation system error model is expressed into a scalar estimation mode by using the scalar estimation algorithm; and then on the basis of the scalar estimation mode, an INS/GNSS combined navigation system is constructed and thus a navigation body navigation system error is compensated. According to the invention, a filter divergence problem caused by inaccurate noise statistic characteristic determination of the traditional kalman filtering algorithm can be solved; reliability of the combined navigation system is improved; and the combined navigation system cost is reduced. A novel combined navigation method is provided.

The invention relates to an all-digital adjustable laser ranging system, which includes an avalanche high-voltage module, a photoelectric detector, an adjustable gain amplifier module, a noise signal detection module, a first AD sampling module, an echo signal detection module, and a second AD sampling module. module, the information processing circuit is used to adjust the avalanche voltage provided by the avalanche high voltage module to the photodetector according to the number of noises counted by the first AD sampling module, and output a corresponding avalanche high voltage adjustment signal to the avalanche high voltage module; 2. The AD sampling module counts the number of echoes, adjusts the amplification gain of the gain-adjustable amplifying module, and outputs a corresponding time-varying gain control signal to the gain-adjustable amplifying module. The main function of this system is to complete the detection of target distance and target reflection intensity. The laser echo signal reflected by the target received by the photoelectric D detector, after automatic gain amplification and AD sampling, is used to time the flight time of the laser pulse and detect the amplitude width signal, so as to calculate the distance information and target reflection characteristics of the target.

A plurality of sensors and a digital adaptive tuning filter bank are used in extracting a desired emitted signal embedded in a noisy environment. By monitoring noise statistics of the sensor signals, the digital adaptive tuning filter bank automatically adjusts its upper (to eliminate strong tonals) and lower (to eliminate background noise) thresholds to obtain a discovery frequency band. The filter bank is designed by examining the discovery band across the sensors and over a predefined period of time. The method described significantly reduces the possibilities of matching self-noise transients (unwanted signals) and thus minimizes the false alarm rate in emitted signal recognition.