Xerox Corporation

Dolby Laboratories Licensing Corporation

Xeorx Corporation

Jon S McElvain

Walter Christian Gish

Charles Michael Hains

Robin Atkins

Gregory John Ward

David Ruhoff

Robert P Loce

Guo-Yau Lin

David E Rumph

Christopher J Vogt

Jonathan Robert Miller

Reiner Eschbach

Shen-Ge Wang

Beilei Xu

Peter A Crean

Vishal Monga

Kimball Darr Thurston, Iii

Jaclyn Anne Pytlarz

George L Eldridge

David Jordan Lieberman

James R Low

Mark A Gwaltney

William E Nelson

Connie F Purdum

Terry C Wells

David Brooks

Robert Wanat

Manu Parmar

Qiqin Dai

Joonsoo Kim

Jared Boone

A method for generating a high-dynamic-range (HDR) image includes (a) denoising a short-exposure-time image, wherein the denoising comprises applying a first guided filter to the short-exposure-time image, the guided filter utilizing a long exposure-time-image as its guide, (b) after the step of denoising, scaling at least one of the short-exposure-time image and the long-exposure-time image to place the short-exposure-time image and the long-exposure-time image on a common radiance scale, and (c) after the step of scaling, merging the short-exposure-time image with the long-exposure-time image to generate the HDR image.

High-dynamic-range image generation with pre-combination denoising

An auto exposure method for a spatially-multiplexed-exposure (SME) high-dynamic-range (HDR) image sensor includes (a) retrieving raw image data from an exposure of the spatially-multiplexed-exposure high-dynamic-range image sensor, (b) preprocessing long-exposure pixel values and short-exposure pixel values of the raw image data to remove therefrom long-exposure pixel values and short-exposure pixel values failing to meet one or more quality requirements, (c) synthesizing, into an high-dynamic-range histogram, the long-exposure pixel values remaining after the step of preprocessing and the short-exposure pixel values remaining after the step of preprocessing, (d) deriving a goodness metric from the high-dynamic-range histogram, (e) adjusting at least one of the long exposure time and the short exposure time, based at least in part upon the goodness metric, and (f) outputting the at least one of the long exposure time and the short exposure time, as adjusted, to the spatially-multiplexed-exposure high-dynamic-range image sensor.

Auto exposure of spatially-multiplexed-exposure high-dynamic-range image sensor metric and adjustment

Imaging methods and devices with pixels divided into pixel groups are disclosed. A pixel group-based global shutter and pixel group-wise staggered long and short exposure followed by readout of two samples per pixel are presented. Example methods and devices for a Bayer color filter array divided into groups of n&#xd7;n pixels are provided.

CMOS sensor architecture for temporal dithered sampling

An auto exposure method for an image sensor includes (a) evaluating variance, for each of a plurality of histograms of the pixel values from a respective plurality of individual exposures of the image sensor at respective exposure 5 time settings, of contribution from individual bins of the histogram to total entropy of the histogram, to determine an optimal exposure time for the image sensor corresponding to a minimum value of the variance, and (b) outputting the optimal exposure time to the image sensor.

Auto exposure of image sensors based upon entropy variance

Input scene images are captured from an original scene. The input scene images may be represented in an input color space. The input scene images are converted into color-space-converted scene images in one of an LMS color space, an ICtCp color space, etc. Scene light levels represented in the color-space-converted scene images are mapped, based at least in part on an optical transfer function, to mapped light levels. A tone mapping is applied to the mapped light levels to generate corresponding display light levels to be represented in display images. The display images may be rendered on a target display.

Perceptually preserving scene-referred contrasts and chromaticities

Methods for recovering saturated pixel values in raw pixel data are described. Given an image with raw pixel values captured using sensors with a color filter array (CFA), image regions are classified according to how many color channels of the CFA are saturated. Values of saturated pixels are estimated based on weighted color ratios of unsaturated pixels and recovered pixels. Example methods for a Bayer CFA are provided.

Dynamic range extension of partially clipped pixels in captured images

A computer-implemented method for bit-depth efficient image processing includes a step of communicating at least one non-linear transformation to an image signal processor. Each non-linear transformation is configured to, when applied by the image signal processor to a captured image having sensor signals encoded at a first bit depth, produce a nonlinear image that re-encodes the captured image at a second bit depth that may be less than the first bit depth, while optimizing allocation of bit depth resolution in the nonlinear image for low contour visibility. The method further includes receiving the nonlinear image from the image signal processor, and applying an inverse transformation to transform the nonlinear image to a re-linearized image at a third bit depth that is greater than the second bit depth. The inverse transformation is inverse to the nonlinear transformation used to produce the nonlinear image.

Bit-depth efficient image processing

A method for compensation of liquid crystal display response variations in high brightness fields, comprising receiving an image signal having a set of initial liquid crystal display code values for a region, estimating individual backlight power levels for the region of the image signal, determining a combined backlight power level based on the individual backlight power levels of the region, determining at least one change in transmittance based on the combined backlight power level of the region and correcting the set of initial liquid crystal display code values based in the determined at least one change in transmittance.

Compensation for liquid crystal display response variations under high brightness light fields

A display system with temperature compensation includes (a) a backlight unit containing a light emitting diode (LED) array, (b) a liquid crystal display (LCD) containing a plurality of pixels for spatially modulating, according to respective LCD drive values of the pixels, transmission of light generated by the LED array, (c) a plurality of temperature probes mounted to the backlight unit for measuring a respective plurality of temperatures at the LED array, (d) a light-field simulator for simulating, at least in part based upon the temperatures, a light field at the LCD as generated by the LED array, and (e) an LCD drive solver for processing a target image and the light field simulated by the light-field simulator, to determine the LCD drive values required to display the target image as compensated for temperatures of the LED array.

Temperature-compensated LED-backlit liquid crystal displays

A memory footprint of look up tables for color transforms can be reduced by separating the look up tables into factors, applying frequency domain transforms, dividing the look up tables into zones, or establishing hierarchical levels with increasing resolution. The methods can be applied to still image or video cameras with limited computation resources that can benefit from reduced memory footprints.

2D LUT color transforms with reduced memory footprint

A substantially rectangular spectral representation is synthesized, which is adapted to produce image capture device sensor outputs if applied to an image capture device. The synthesized substantially rectangular spectral representation can be utilized in generating output color values of an output color space from image capture device sensor outputs, where the image capture device sensor outputs correspond to an image captured by an image capture device. The generated output color values correspond to colors perceived by the human visual system for the same image as that captured by the image capture device. Image capture device gamut is also determined.

Spectral synthesis for image capture device processing

In a digital camera system, traditional input color transformations of raw data using a 3&#xd7;3 matrix are replaced by two-dimensional lookup (2-D LUTs) tables that better approximate spectral reflectance characteristics. Chromaticity-like signals and an input scale factor are generated in response to raw sensor output values. The chromaticity signals and the input scale factor are mapped via 2-D LUTs into preliminary output signals and an output scaling factor. A final set of output color signals is generated in response to the preliminary output signals and the output scale factor. The 2-D LUTs are designed in response to spectral response curves generated on both the input and output side of the color transformation.

Two-dimensional color transformations for wide gamut workflows in digital cameras

Disclosed herein is a method that includes a classifier that distinguishes objects based on their color shades. A moving window based operator looks at the color class of the adjacent objects and determines if an adjustment is necessary. If at least one of the objects is the type of interest and the color classes of the objects meet the criteria, then the boundary between the objects is subject to be adjusted. Two adjustment modes are discussed: 'adjust-tagging' mode and 'adjust-color' mode. In the adjust-tagging mode, a specific tag is sent down to the print engine to force the use of high addressability halftones for the boundary pixels in the darker object. In the adjust-color mode, a color lighter (usually white) than that of the lighter object is assigned to the boundary pixels in the lighter object. The width of the modified pixels along the boundary is configurable.

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Manhattan Beach, CA, United States of America

Jon S McElvain