152 results about "Spatial noise" patented technology

In one embodiment, a directional microphone array having (at least) two microphones generates forward and backward cardioid signals from two (e.g., omnidirectional) microphone signals. An adaptation factor is applied to the backward cardioid signal, and the resulting adjusted backward cardioid signal is subtracted from the forward cardioid signal to generate a (first-order) output audio signal corresponding to a beampattern having no nulls for negative values of the adaptation factor. After low-pass filtering, spatial noise suppression can be applied to the output audio signal. Microphone arrays having one (or more) additional microphones can be designed to generate second- (or higher-) order output audio signals.

A method of reducing spatial noise in an image. Low-pass (smoothing) filters are calculated simultaneously from three successive image rows. Three blocks (m1, m2, m3) are associated with the three successive image rows, and the blocks are processed in row-major order. This implementation is applicable to both luminance and chrominance. The number of smoothing parameters is reduced to one. The technique is applicable to both luminance and chrominance. Directional mapping is used. Extension of the technique to spatial filtering using a 5×5 neighborhood (using five successive image rows) is described. Embodiments of the method using the MMX instruction set are described.

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.

A technique is provided for programmably horizontally filtering pixel values of frames of a plurality of video frames. The technique includes, in one embodiment, passing pixel values through a real-time horizontal filter disposed as preprocessing logic of a video encode system. The horizontal filter is programmable and includes a filter coefficients buffer for holding multiple sets of filter coefficients. The horizontal filter programmably employs the multiple sets of filter coefficients to selectively perform spatial noise filtering, or spatial noise filtering and image scaling on the pixels. The filter coefficients are also programmable and may be changed dynamically and repeatedly, with changes being applied at frame boundaries. When performing image scaling, multiple sets of filter coefficients are employed.

A content-dependent scan rate converter with adaptive noise reduction that provides a highly integrated, implementation efficient de-interlacer. By identifying and using redundant information from the image (motion values and edge directions), this scan rate converter is able to perform the tasks of film-mode detection, motion-adaptive scan rate conversion, and content-dependent video noise reduction. Adaptive video noise reduction is incorporated in the process where temporal noise reduction is performed on the still parts of the image, thus preserving high detail spatial information, and data-adaptive spatial noise reduction is performed on the moving parts of the image. A low-pass filter is used in flat fields to smooth out Gaussian noise and a direction-dependent median filter is used in the presence of impulsive noise or an edge. Therefore, the selected spatial filter is optimized for the particular pixel that is being processed to maintain crisp edges.

The invention requests to protect a neural network nonuniformity correction method based on scene statistics, belonging to the infrared focal plane detection field. The invention provides the method aiming at the shortcoming that the traditional neural network correction method is difficult to eliminate low-frequency spatial noises. The method comprises the following steps: initializing image related matrices and parameters; detecting and compensating blind pixels according to the image pixel gray value; carrying out nonuniformity correction on image offset by a scene statistics method; judging the regional attributes of the pixels by a neural network correction method according to the standard deviation thresholds of correction errors, and carrying out nonlinear gain correction on the images which are corrected based on scene statistics and contain no low-frequency spatial noises. By the method, the correction effects of the desired image signals are good, target fade-out and ghostingare inhibited, and the changes of the background images hardly affect the correction effects. The method can be widely applied to image detection and processing.

In one embodiment, a directional microphone array having (at least) two microphones mounted on opposite sides of a device generates forward and backward base signals from two (e.g., omnidirectional) microphone signals using diffraction filters and equalization filters. Each diffraction filter implements a (possibly different) transfer function representing the response of an audio signal traveling from a corresponding microphone around the device to the other microphone. A scale factor is applied to, for example, the backward base signal, and the resulting scaled backward base signal is combined with (e.g., subtracted from) the forward base signal to generate a first-order differential audio signal. After low-pass filtering, spatial noise suppression can be applied to the first-order differential audio signal. Microphone arrays having one (or more) additional microphones can be designed to generate second- (or higher-) order differential audio signals.

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 relates to matrix image sensors of the bolometric type, in which each pixel comprises a bolometric resistor whose value varies according to the thermal flow received by the pixel. The resistor is biased by a bias voltage of value Vpol. The current that passes through it is compensated for by a compensation current Icomp, the difference between these currents being integrated in order to produce a measurement signal. The bias voltage (or the compensation current) is adjusted pixel by pixel, for example during a calibration phase, so that all the pixels have an apparent identical sensitivity despite the dispersion of the nominal value of the bolometric resistor. The adjustment is carried out in an analogue manner by storing an individual voltage specific to each pixel in a sensitivity trimmer capacitor specific to this pixel. The capacitor acts directly on the adjustment of the bias voltage or on other parameters playing a role in the sensitivity of the pixel (integration time for example). The spatial noise is thereby considerably reduced.

The present invention relates to method of adaptive noise measurement of a video signal comprising a sequence of images. The proposed method comprises the steps of searching one or more spatially uniform areas within a current image, determining a spatial uniformity value of said one or more spatially uniform areas and combining said spatial uniformity values to obtain a spatial noise value representing a first measure for the amount of noise in said current image, detecting motion by comparing the current image with one or more preceding images and/or one or more succeeding images of said sequence of images by use of a motion threshold to obtain one or more motion maps indicating the amount of motion between the current image and the respective preceding or succeeding image, searching one or more static areas within said one or more motion maps, determining a temporal uniformity value for said one or more static areas by comparing image values at positions in said one or more preceding images and/or said one or more succeeding images, at which a low amount or no motion is indicated in the respective one or more motion maps, said temporal uniformity value representing a measure for the amount of noise at said positions in said current image, and combining said obtained temporal uniformity values to obtain a combined noise value representing a second measure for the amount of noise in said current image.

A adaptive noise reduction filter is provided for reducing noise in a video signal. Each pixel in a portion of a video frame is evaluated to determine a likelihood L of impulse noise corruption to each pixel. A total number P of pixels in the video frame that have a likelihood of impulse noise corruption is determined. One of a plurality of spatial noise reduction filters is selected to use on the video frame based on the total number P and on the likelihood L of impulse noise corruption to a current pixel. A motion value for each of the pixels in the portion of the video frame may be determined and used to inhibit spatial noise reduction filtering of each pixel that has a low motion value.

An image processing apparatus and a processing method thereof are provided. The image processing apparatus includes an image capturing module, an image separation module, an image stabilization module, a temporal noise reduction module, and a spatial noise reduction module. The image capturing module captures a plurality of Bayer pattern images. The image separation module decreases the Bayer pattern images in size and transforms them into a plurality of YCbCr format images. The image stabilization module receives Y channel images of the YCbCr format images and the Bayer pattern images to perform motion estimation, to produce a plurality of global motion vectors (GMVs). The temporal noise reduction module performs temporal blending process on the Bayer pattern images according to the GMVs, to produce first noise reduction images. The spatial noise reduction module performs 2-dimensional spatial noise reduction on the first noise reduction images to produce second noise reduction images.

The invention is related to a device and a method for reducing direction dependent spatial noise. The proposed device includes a plurality of microphones (2,3,4,5) for measuring an acoustic input signal (XR1,XR2,XL1) from an acoustic source (7). The plurality of microphones (2,3,4,5) form at least one monaural pair (2,3) and at least one binaural pair (2,4). Di not rectional signal processing circuitry (71,72,73,74) is provided for obtaining, from said input signal (XR1,XR2,XL1), at least one monaural directional signal [Y1,N1) and at least one binaural directional signal (Y2, N2). A target signal level estimator (76) estimates a target signal level (Fs) by combining at least one of said monaural directional signals (Y1) and at least one of said binaural directional signals (Y2), which at least one monaural directional signal (Y1) and at least one binaural directional signal (Y2) mutually have a maximum response in a direction of said acoustic source (7). A noise signal level estimator (75) estimates a noise signal level (FD) by combining at least one of said monaural directional signals (N1) and at least one of said binaural directional signals (N2), which at least one monaural directional signal (N1) and at least one binaural directional signal (N2) mutually have a minimum sensitivity in the direction of said acoustic source (7).

A system and method for performing optical navigation uses a spatial noise pattern estimate of the spatial noise pattern in captured frames of image data caused by contamination on at least one component of the system to substantially remove the spatial noise pattern in the captured frames of image data before the captured frames of image data are used for displacement estimation.