University of California

Pixon Imaging, Inc.

Micro Usa, Inc.

Pixon, LLC

Other

Micro Biz Ventures LLC

Richard C Puetter

Amos Yahil

Vesa Junkkarinen

Paul Babyn

Robert Piña

Carl A Wesolowski

Robert K Pina

A method and system for enhancing spatial resolution of an image of an object includes using an image detector with a sensor array of pixels configured for extended dynamic range detection. Multiple image sample are collected to assemble a stack of image samples and input into an image processor for processing using an image reconstruction engine to generate a single output image from the stack. In a preferred embodiment, the image reconstruction engine executes the Pixon method for image reconstruction.

Method and system for processing images from a remote image detector

An image sensor comprises a pixel array having a plurality of pixel regions, wherein the pixel array is adapted to generate at least one signal from each pixel region and a separate signal from a subset of pixels within each pixel region, both during a single exposure period. In one embodiment, the sensor is in communication with a shift register which accumulates the separate signal and transfers the separate signal to an amplifier. The shift register further accumulates the at least one signal from the pixel region after the separate signal has been transferred to the amplifier and transfers the at least one signal to the amplifier.

Device and method for extending dynamic range in an image sensor

A method for spatially-adaptive tone mapping in an image having high dynamic range includes using a computing device to receive an input image from an image sensor comprising a plurality of pixels having pixel locations and determine within the input image a plurality of local size scales, each comprising a neighborhood having substantially constant illumination. The variation in reflectance within each neighborhood is estimated and local contrast within each neighborhood is enhanced. Using the illumination and variation within the contrast-enhanced neighborhoods, the image is remapped to a reduced dynamic range to generate an output image.

Spatially adaptive tone mapping operator

A method for determining a level of haze within a field of view of an image sensor adapted for detecting a plurality of color components includes collecting a first image of the field of view at a first point in time, defining regions of the first image as a reference and determining reference intensities for each color component. At least one second image is collected at different points in time and intensities for each color component are determined and compared against the reference intensities. If the intensities of second image differ from the reference intensities by more than a predetermined threshold, all or a portion of the second image is designated as being obscured by haze and requiring correction.

Imaging through aerosol obscurants

Plasma concentration of a compound of interest is measured in two or ideally 4 or more blood samples taken from a patient over a period of time following bolus injection. The measured values are input into a computer processor programmed to execute a computer program comprising an algorithm that uses the gamma variate (GV) function to model drug plasma concentration, then uses Tikhonov regularization to perform the fit, selecting a regularization constant so that the relative error in the plasma clearance is minimized. One or more output values representative of renal function are generated.

Method for evaluating renal function

A method is provided for decompressing an image originally compressed using a known compression technique. An original compressed image file is defined by a plurality of locations that correspond to image data. A set of smooth test images is created by iteratively applying a plurality of different size PIXON&#xae; kernels to each location of a starting image beginning with a smallest size kernel. Each smooth test image is compressed using the known compression technique, then compared against the original compressed image at each location of the image to determine a goodness-of-fit of the compressed smooth test image to each location of the original compressed image file. For each location, the smooth test image that has the largest size kernel that passes a pre-determined goodness-of-fit criterion is selected for a given location of the decompressed image. A compression method utilizes the PIXON&#xae; method for optimization of knot points within an image. The optimized knot points are used to interpolate image intensity between the knot points.

Image compression and decompression using the PIXON method

A computer-based system and method are provided to determine the minimum number of factors required to account for input data by seeking an approximate minimum complexity model. In an exemplary embodiment, covariance in the daily returns of financial securities is estimated by generating a positive-definite estimate of the elements of a covariance matrix consistent with the input data. Complexity of the covariance matrix is minimized by assuming that the number of independent parameters is likely to be much smaller than the number of elements in the covariance matrix. Each variable is described as a linear combination of independent factors and a part that fluctuates independently. The simplest model for the covariance matrix is selected so that it fits the data to within a specified level as determined by the selected goodness-of-fit criterion.

System and method for prediction of behavior in financial systems

Input data comprising a video signal is processed using a combination of a known image processing method to deblur, or sharpen, the image and convolution with Pixon&#x2122; kernels for smoothing. The smoothing process utilizes a plurality of different size Pixon&#x2122; kernels which operate in parallel so that the input data are convolved with each different Pixon&#x2122; kernel simultaneously. The smoothed image is convolved with the point response function (PRF) to form data models that are compared against the input data, then the broadest Pixon&#x2122; kernel that fits the input data within a predetermined criterion are selected to form a Pixon&#x2122; map. The data are smoothed and assembled according to the Pixon&#x2122; map, then are deconvolved and output to a video display or other appropriate device, providing a clearer image with less noise.

High speed signal enhancement using pixons

The method identifies a Pixon element, which is a fundamental and indivisible unit of information, and a Pixon basis, which is the set of possible functions from which the Pixon elements are selected. The actual Pixon elements selected from this basis during the reconstruction process represents the smallest number of such units required to fit the data and representing the minimum number of parameters necessary to specify the image. The Pixon kernels can have arbitrary properties (e.g., shape, size, and/or position) as needed to best fit the data.

Accelerated signal encoding and reconstruction using pixon method

The method identifies a Pixon element, which is a fundamental and indivisible unit of information, and a Pixon basis, which is the set of possible functions from which the Pixon elements are selected. The actual Pixon elements selected from this basis during the reconstruction process represents the smallest number of such units required to fit the data and representing the minimum number of parameters necessary to specify the image. The Pixon kernels can have arbitrary properties (e.g., shape size, and/or position) as needed to best fit the data.

The method identifies a pixon, a fundamental and indivisible unit of information, and a pixon basis, which represents the smallest number of cells of arbitrary shape or position required to fit the data, representing the minimum degrees of freedom necessary to specify the image. The goodness-of-fit is determined according to the Maximum Residual Likelihood (MRL) statistic or a similar chi-squared statistic.

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San Diego, CA, United States of America

Richard C Puetter