130 results about "Adaptive denoising" patented technology

An adaptive noise canceling microphone system for extracting a desired signal, in particular a desired speech signal, comprising two microphones being arranged at a predefined distance from each other; a signal forming system (SFS) being adapted to receive a first and second input signals resulting from sounds received by the two microphones wherein an acoustical signal component in the first input signal is determined, wherein an acoustical signal component in the second input signal is determined, wherein the acoustical signal component in the first input signal is enhanced to generate a speech enhanced signal, and wherein the acoustical signal component in the second input signal is suppressed to generate a speech nulled signal; an adaptive noise cancellation filtering circuit being adapted to receive the speech enhanced signal and the speech nulled signal, wherein the noise in the speech enhanced signal is cancelled using the speech nulled signal as reference, thereby generating an output filtered signal representing the desired signal.

A centralized wireless communication system for a host device having a host processor and one or more host wireless communication modules includes a controller, one or more antenna elements, and an RF multiplexer coupled to the one or more antenna elements. The RF multiplexer includes one or more ports and is configured to establish an RF communication path between one or more ports and one or more antenna elements based on instructions from the controller. The centralized wireless communication system can provide adaptive noise cancellation and/or operate antenna elements as part of an active phased array.

A non-iterative 3D processing method and system is disclosed for generic noise reduction. The 3D noise reducer is based on a simple conversion of the five types of noise to equivalent additive noise of varying statistics. The proposed technique comprises also an efficient temporal filtering technique which combines Minimization of Output Noise Variance (MNV) and Embedded Motion Estimation (EME). The proposed temporal filtering technique may be furthermore combined with classical motion estimation and motion compensation for more efficient noise reducer. The proposed technique comprises also a spatial noise reducer which combines Minimum Mean Squared Error (MMSE) with robust and effective shape adaptive windowing (SAW) is utilized for smoothing random noise in the whole image, particularly for edge regions. Another modification to MMSE is also introduced for handling banding effect for eventual excessive filtering in slowly varying regions.

The present technology provides adaptive noise reduction of an acoustic signal using a sophisticated level of control to balance the tradeoff between speech loss distortion and noise reduction. The energy level of a noise component in a sub-band signal of the acoustic signal is reduced based on an estimated signal-to-noise ratio of the sub-band signal, and further on an estimated threshold level of speech distortion in the sub-band signal. In embodiments, the energy level of the noise component in the sub-band signal may be reduced to no less than a residual noise target level. Such a target level may be defined as a level at which the noise component ceases to be perceptible.

A personal audio device, such as a wireless telephone, includes noise canceling circuit that adaptively generates an anti-noise signal from a reference microphone signal and injects the anti-noise signal into the speaker or other transducer output to cause cancellation of ambient audio sounds. A processing circuit monitors a level of the anti-noise signal, determines that the anti-noise signal may cause damage to the transducer and adjusts the generation of the anti-noise signal such that damage to the transducer is prevented.

An improved temporal noise reduction method and system detects the global motion and adjusts the overall gain of the temporal filtering. Temporal noise reduction is applied to two video frames, wherein one video frame is the current input noisy frame, and the other video frame is a previous filtered frame stored in memory. In this method, noise estimation is first performed to estimate the noise variance/standard deviation in the input video sequence. Then, motion estimation is applied to obtain the motion vectors indicating relative motion between the pixels in the current noisy frame and the corresponding pixels in the previous noise-reduced frame. From such motion vectors, global motion estimation is applied to estimate the camera motion of the video sequence. If reliable global motion is obtained, the overall gain of the temporal filtering is reduced by adjusting the estimated noise level. Motion blur is thus prevented.

An image capturing system includes: a first noise reduction unit which roughly removes the effects of an edge component by performing edge-preserving adaptive noise reduction processing on a target pixel within a local region including the target pixel and the neighboring pixels extracted from an image signal acquired from a CCD; a noise estimation unit which dynamically estimates the noise amount with respect to the target pixel based upon the target pixel value thus subjected to the noise reduction processing by the first noise reduction unit; and a second noise reduction unit which performs noise reduction processing on the target pixel based upon the target pixel value thus subjected to the noise reduction processing by the first noise reduction unit and the noise amount thus estimated by the noise estimation unit.

The invention discloses a method and device for performing adaptive denoising on a video image. The device comprises a video image reading module, an edge detection module, a filtering mode selection module and a filtering module; the method comprises the following steps of: reading in a frame of video image from a buffer zone, respectively calculating motion vectors between each pixel in the frame of image and the pixel at the same location in a neighboring frame, and respectively executing the edge detection on each pixel in the frame of image; respectively selecting the filtering mode having corresponding intensity for each pixel according to the edge detection result and the calculation result of the motion vector and of each pixel, and then, correspondingly filtering each pixel by a Gaussian similarity filter. The method and the device have obvious denoising effect on various noises of different scene sequences, and can effectively remove noises by combination of a time domain and a space domain.

The invention discloses an adaptive indoor dynamic target UWB positioning method. The method comprises the following steps: firstly measuring a TDOA value by using a UWB positioning system; then, processing the measured TDOA value by using a wavelet analysis adaptive denoising method, outputting a reconstructed TDOA value, converting the reconstructed TDOA value into a distance difference, establishing a nonlinear equation set, and obtaining an optimal solution of the equation set; then using the optimal solution as an initial value, performing tracking and positioning on a dynamic target by using an extended Kalman filtering algorithm, and figuring out a final estimated value; and finally outputting the final estimated value. By virtue of the adaptive indoor dynamic target UWB positioning method provided by the invention, a distance measuring error caused by the influence of multipath propagation (Multipath) and non-line-of-sight interference (NLOS) in a UWB signal propagation process can be weakened and even eliminated, the positioning precision is improved, and the accurate positioning of the dynamic target in a non-line-of-sight indoor environment can be realized. The ultra wide band positioning system verifies the validity of the positioning method and realizes the indoor positioning of the dynamic target.

A clear, high quality voice signal with a high signal-to-noise ratio is achieved by use of an adaptive noise reduction scheme with two microphones in close proximity. The method includes the use of two omini directional microphones in a highly directional mode, and then applying an adaptive noise cancellation algorithm to reduce the noise.

An image signal processing device includes a signal processing circuit for executing signal processing for correction and recording on image data representative of an input image. Also, the signal processing circuit executes noise reduction on the individual image data in accordance with image inputting conditions and/or a pixel level. In the signal processing circuit, a threshold generating circuit generates a threshold by taking account of the image inputting conditions and/or a pixel level. During noise reduction, a noise reducing circuit produces a difference in level between the subject pixel data whose noise is to be detected and the mean value of the subject pixel data and pixel data around the subject pixel. The noise reducing circuit then selects either one of the subject pixel data and mean value in accordance with the difference and threshold.

A method of adaptively reducing noise within an x-ray image includes receiving raw data (R) representing a detected x-ray signal from an object. A counts-based modulation mask (M_cb) is generated in response to the raw data (R). In one embodiment, a structure dependent noise filtered image (I_blended) is generated in response to the raw data. A noise-reduced image (I_F) is generated in response to the counts-based modulation mask (M_cb) and the structure dependent noise filtered image (I_blended). In another embodiment, a structure gradient mask (M_cs) is generated in response to the raw data (R). The noise-reduced image (I_F) is generated in response to the counts-based modulation mask (M_cb) and the structure gradient mask (M_cs).

A clear, high quality voice signal with a high signal-to-noise ratio is achieved by use of an adaptive noise reduction scheme with two microphones in close proximity. The method includes the use of two omini directional microphones in a highly directional mode, and then applying an adaptive noise cancellation algorithm to reduce the noise.

A noise reduction system is provided. In a temporal module, a temporal characteristic detector detects a temporal characteristic of the input image based on the input image and a reference image. A temporal filter performs temporal noise reduction on the input image based on the reference image and the temporal characteristic to generate a temporal filtered image. A temporal selector selects the temporal filtered image or the input image as a preliminary output accordingly. In a spatial module, a spatial characteristic of the input image is detected. A spatial filter performs spatial noise reduction on the input image based on the preliminary output and the spatial characteristic to generate a spatial filtered image. A spatial selector selects the spatial filtered image or the preliminary output as the output image based on the temporal or spatial characteristics.

The invention discloses an adaptive denoising method for an ultrasonic image. The adaptive denoising method comprises the following steps: (1) obtaining the ultrasonic image; (2) performing Gaussian-Laplacian pyramid decomposition on the ultrasonic image and obtaining Gaussian layers and Laplacian layers at different scales; (3) calculating a structure tensor and a diffusion tensor of the Gaussian layer at each scale and performing anisotropic diffusion filtering treatment on the Gaussian layer at the scale; (4) according to a characteristic value of the structure tensor of the Gaussian layer, designing a grey mapping curve, and performing grey mapping on the Gaussian layer at the scale according to the grey mapping curve; (5) repeating the steps (3) and (4) for multiple times and performing the same treatment on the Gaussian layer and the Laplacian layer at each scale; (6) performing reverse reconstruction on the treated Gaussian layer and Laplacian layer and obtaining a denoised ultrasonic image; (7) outputting the denoised ultrasonic image. The adaptive denoising method can inhibit edge enhancement and spots of the image and is simple in algorithm and strong in adaptability.

Systems, methods, and devices for providing noise reduction to an audio signal, such as a speech signal, to improve the accuracy of a speech recognition system. The various embodiments may be particularly useful for training and simulation systems.

Electronic images that are degraded by noise and data reduction, such as MPEG encoding, display artifacts in the reproduced image, such as ringing (“ripples”) and blocks (“huge pixels”), and noise in the image may be apparent as graininess. By performing image analysis, both on a frame-by-frame and pixel-by-pixel basis it is possible to identify and separate edges in the image, ringing artifacts and the boundaries between block transitions. By applying noise reduction according to the analysis, followed by sharpness enhancement, it is possible to clean up the image for further utilization.

The invention provides an adaptive hybrid norm dictionary learning seismic wave impedance inversion method, and belongs to the field of seismic inversion. Through the combination of the post-stack wave impedance inversion in exploration geophysics with dictionary learning and denoising, the adaptive denoising and dictionary learning based post-stack wave impedance inversion method can be provided.The method adopts an adaptive hybrid norm objective function to replace an original 2-norm objective function, so that an objective function can better overcome non-gaussian noise; well logging information is introduced through a dictionary learning method, so that influences of different seismic noise can be effectively overcome, and the vertical resolution of inversion can be enhanced by fullyutilizing the well logging information; and compared with traditional methods of least squares, robustness can be stronger, the noise immunity and inversion result precision of the seismic wave impedance inversion can be enhanced, multiple solutions can be reduced, and convenient data processing in geological exploration can be achieved.

The invention discloses a self-adaption noise reduction device for low frequency noise in an indoor environment. The self-adaption noise reduction device for low frequency noise in the indoor environment comprises a primary microphone, an inverse sound source, a frequency adjusting device and a secondary microphone. The secondary microphone is a high-sensitivity omnidirectional electret secondary microphone with the response frequency ranging from 20Hz to 20KHz. The primary microphone and the secondary microphone are connected through an audio cable and a 16-bit analog-to-digital converter respectively. The 16-bit analog-to-digital converter is connected with an I/O read-write parallel interface of the frequency adjusting device through a 16-pin parallel interface bus. The reverse sound source is connected with an MCU processor of the frequency adjusting device through a serial analog-to-digital converter. The self-adaption noise reduction device for low frequency noise in the indoor environment is particularly suitable for low frequency noise processing in a household environment, low frequency noise emitted by indoor electronic devices and electrical devices can be obviously reduced, the living environment is improved, and the living quality is improved. The self-adaption noise reduction device for low frequency noise in the indoor environment is small in size, capable of being used once a system is powered, free of a complex installation process, low in manufacturing cost and capable of being completely purchased and deployed by general users.

The invention discloses a hybrid network station frequency hopping parameter blind estimation method based on short-time Fourier transform (STFT) and smooth pseudo Wigner distribution (SPWVD), which is used for solving the problems of low frequency hopping parameter estimation precision and high complexity under the condition of low signal-to-noise ratio in the prior art. According to the implementation scheme, the method comprises the following steps of firstly, transforming an antenna receiving signal into a time-frequency domain by using the short-time Fourier transform, and carrying out the adaptive noise reduction processing on the time-frequency signal so as to improve the anti-noise performance of a system; secondly, carrying out fine estimation on the frequency through a K-means mean value clustering algorithm; then extracting the time-frequency information according to the finely estimated frequency to obtain the time-hopping coarse estimation; and finally, carrying out fine estimation on a hopping moment by adopting the smooth pseudo Wigner transformation and a corrected truncation threshold. According to the method, the complexity is reduced, the frequency resolution isincreased, the estimation precision of the frequency hopping moment is improved, and the method can be used for the parameter estimation of the frequency hopping signal in a complex electromagnetic environment.