Abstract: A high dynamic range (HDR) compression method and apparatus modeled after the heat equation describing temperature changes in a thin plate. This approach allows combining high dynamic range compression together with noise reduction in a single process, to be performed within the same iteration of the heat equation. Noise reduction is of particular concern while performing HDR compression because brightening of dark areas during high dynamic range compression has the potential to increase noise levels. Performing image processing techniques in combination according to the invention provides enhanced results while lowering the overall processing overhead. This innovation extends the heat equation analogy by adding anisotropic diffusion as an additional term, which allows joint operation of HDR and NR and mitigates noise enhancement within HDR compression during shadow enhancement.

Abstract: A system for and method of determining calibration parameters while only capturing a single image is described herein. Furthermore, traditional calibration algorithms are avoided by the direct determination of the calibration parameters. The determination of the calibration parameters is possible by first determining a training data set from images acquired of a variety of objects with a multitude of colors. Then, using the training data set, regression coefficients are generated. A camera to be calibrated then acquires only one set of image information such as a single picture. Then, using the regression coefficients and the acquired information, the calibration parameters are directly estimated for that camera.

Abstract: A method of evaluating halo artifacts is described herein. The method utilizes a pattern of color patches, a color space and color difference metrics to analyze color changes which correlate to the amount of halo. The pattern of color patches is utilized in the CIE L*a*b* color space to determine an area of patch unaffected by halo of the pattern of color patches. After the area of patch unaffected by halo is determined, a Reference Value is computed by averaging the CIE L*a*b* color for the area of patch unaffected by halo. Then an Artifact Value is calculated either by averaging the CIE L*a*b* color for the area outside the area of patch unaffected by halo but before the margin or by averaging the CIE L*a*b* color on the edge of the patch. Once these values are determined, the halo quantity is calculated.

Abstract: A signal processing system includes: defining a nonlinear function; defining a set of requirements for an output signal; obtaining an input signal; applying a cubic polynomial fitting to approximate the nonlinear function and provide an approximated nonlinear function; assigning a set of fitted polynomial parameters to the approximated nonlinear function; transforming the input signal with the approximated nonlinear function to provide a transformed signal; modifying the transformed signal by adjusting the set of fitted polynomial parameters to provide a modified signal meeting the set of requirements for the output signal; and outputting the modified signal.

Abstract: A camera and method which selectively applies image content adjustments to elements contained in the image material. By way of example, the method involves registration of user touch screen input and determination of the arbitrary extent of a specific element in the captured image material at the location at which touch input was registered. Once selected, the element can be highlighted on the display, and additional user input may be optionally input to control what type of adjustment is to be applied. Then the element within the captured image material is processed to apply automatic, or user-selected, adjustments to the content of said element in relation to the remainder of the captured image. The adjustments to the image element may comprise any conventional forms of image editing, such as saturation, white balance, exposure, sizing, noise reduction, sharpening, blurring, deleting and so forth.

Abstract: In one embodiment, an apparatus for three-dimensional (3-D) image acquisition can include: (i) first and second lenses configured to receive light from a scene; (ii) first, second, third, and fourth sensors; (iii) a first beam splitter arranged proximate to the first lens, where the first beam splitter can provide a first split beam to the first sensor, and a second split beam to the second sensor; and (iv) a second beam splitter arranged proximate to the second lens, where the second beam splitter can provide a third split beam to the third sensor, and a fourth and split beam to the fourth sensor. For example, the sensors can include charge-coupled devices (CCDs) or CMOS sensors.

Abstract: In one embodiment, an apparatus for three-dimensional (3-D) image acquisition can include: (i) first and second lenses configured to receive light from a scene; (ii) first, second, third, and fourth sensors; (iii) a first beam splitter arranged proximate to the first lens, where the first beam splitter can provide a first split beam to the first sensor, and a second split beam to the second sensor; and (iv) a second beam splitter arranged proximate to the second lens, where the second beam splitter can provide a third split beam to the third sensor, and a fourth and split beam to the fourth sensor. For example, the sensors can include charge-coupled devices (CCDs) or CMOS sensors.

Abstract: A method and system are provided for approximating spectral sensitivities of a particular image sensor, the image sensor having a color filter array positioned over the image sensor. In one example of the method, the method involves measuring spectral sensitivities of a set of image sensors each having a color filter array positioned over the image sensor, calculating mean spectral sensitivities of the set of image sensors for each color within the color filter array, measuring outputs of a particular image sensor when capturing a picture of a plurality of color patches under a first illuminant and calculating spectral sensitivities of the particular image sensor using the mean spectral sensitivities and the output of the particular image sensor. In some embodiments, the method further comprises utilizing the calculated spectral sensitivities to determine outputs of the particular image sensor under a second illuminant.

Abstract: A spatial transformation methodology provides a new image interpolation scheme, or analyzes an already existing one. Examples of spatial operations include but are not limited to, demosaicing, edge enhancement or sharpening, linear filtering, and non-linear filtering. A demosaicing operation is described herein, although the scheme is applied generally to spatial transformation operations. The spatial transformation methodology includes detailed expressions for the noise covariance after a spatial operation is performed for each of the three color channels, red, green, and blue. A color filter array is in the form of a Bayer pattern and demosaicing is performed using a 4-neighbor bilinear interpolation. Using lattice theory, the spatial transformation methodology predicts noise covariance after demosaicing in terms of the input noise covariance and an autocorrelation function of the image is determined for a given selectable number of shifts.

Abstract: A method for high dynamic range compression uses a modified cumulative histogram as a compression curve. This curve is computed from the cumulative histogram of the image with constraints that the local derivative on the curve does not exceed a certain limit. The limit is fixed along the curve or the limit is variable, taking into account noise characteristics at various pixel values. To provide appropriate detail preservation, a smoothing filter is used to separate the image into an illumination image, referred to as a base image, and a detail image, and the compression curve is applied to the base image only. The compression method provides high dynamic range compression of the image while preserving the global contrast perception. Conventional global algorithms for high dynamic range compression are not capable of achieving this result. The proposed high dynamic range compression method also minimizes noise amplification while lightening the dark areas during image compression.

Abstract: A video input signal is analyzed to detect image content and image properties, wherein detecting the image content includes automatically deriving image features. A content group is determined for the video input signal based on the detected image content, the content group including predefined image properties. The image properties of the video input signal are adjusted based on a difference between the detected image properties and the predefined image properties.

Abstract: In one embodiment, a sub-pixel rendering method includes receiving 3D image data associated with pixel intensity values of N two-dimensional images having multiple sets of corresponding pixels. Each set of corresponding pixels includes N pixels (one pixel from each of N images) and each pixel has a green sub-pixel, a red sub-pixel and a blue sub-pixel. The method further includes mapping, for each selected set, N green sub-pixels, N red sub-pixels and N blue sub-pixels to M sub-pixels on a display to form a stereogram of the scene. The above mapping includes mapping N green sub-pixels from N images to N green sub-pixels on the display, mapping N red sub-pixels from N images to L red sub-pixels on the display, and mapping N blue sub-pixels from N images to K blue sub-pixels on the display, where L does not exceed N and K is lower than N.

Abstract: A method of and system for calibrating an imaging device is described herein. An iterative method that attempts to find the best calibration parameters conditional upon an error metric is used. Regression is used to estimate values in a color space where the calibration is performed based upon a training data set. More calculation steps are required than would be for a regression in raw RGB space, but the convergence is faster in the color space where the calibration is performed, and the advantages using boundary conditions in the color space is able to provide improved calibration.

Abstract: A system for and method of determining calibration parameters while only capturing a single image is described herein. Furthermore, traditional calibration algorithms are avoided by the direct determination of the calibration parameters. The determination of the calibration parameters is possible by first determining a training data set from images acquired of a variety of objects with a multitude of colors. Then, using the training data set, regression coefficients are generated. A camera to be calibrated then acquires only one set of image information such as a single picture. Then, using the regression coefficients and the acquired information, the calibration parameters are directly estimated for that camera.

Abstract: A system for and method of calibrating an imaging device efficiently is described herein. The imaging device acquires an image of an object that is more than one color. The information acquired is then transferred to a computing device. The information is then used to generate a set of data which represents information which was not acquired in the image. The set of data is generated based on statistical prediction using a training data set. Using acquired image information and the set of data, an imaging device is able to be calibrated. Since the process of calibration utilizing this method only requires one image to be acquired and a reduced set of image information to be sent to the computing device, the process is more efficient than previous implementations.

Abstract: A two dimensional/three dimensional (2D/3D) digital acquisition and display device for enabling users to capture 3D information using a single device. In an embodiment, the device has a single movable lens with a sensor. In another embodiment, the device has a single lens with a beam splitter and multiple sensors. In another embodiment, the device has multiple lenses and multiple sensors. In yet another embodiment, the device is a standard digital camera with additional 3D software. In some embodiments, 3D information is generated from 2D information using a depth map generated from the 2D information. In some embodiments, 3D information is acquired directly using the hardware configuration of the camera. The 3D information is then able to be displayed on the device, sent to another device to be displayed or printed.

Abstract: A method of evaluating halo artifacts is described herein. The method utilizes a pattern of color patches, a color space and color difference metrics to analyze color changes which correlate to the amount of halo. The pattern of color patches is utilized in the CIE L*a*b* color space to determine an area of patch unaffected by halo of the pattern of color patches. After the area of patch unaffected by halo is determined, a Reference Value is computed by averaging the CIE L*a*b* color for the area of patch unaffected by halo. Then an Artifact Value is calculated either by averaging the CIE L*a*b* color for the area outside the area of patch unaffected by halo but before the margin or by averaging the CIE L*a*b* color on the edge of the patch. Once these values are determined, the halo quantity is calculated.

Abstract: A system and method for efficiently performing a depth map recovery procedure includes an imaging device that is implemented in a single-lens stereo-shutter configuration for simultaneously capturing overlaid images corresponding to a photographic target. A depth map generator is configured to analyze the overlaid images to determine disparity values corresponding to separation distances between matching points in the overlaid images. The depth map generator then utilizes the disparity values to calculate depth values that correspond to locations in the photographic target. The depth map generator may then utilize the foregoing depth values for creating a depth map corresponding to the photographic target.

Abstract: A spatial transformation methodology provides a new image interpolation scheme, or analyzes an already existing one. Examples of spatial operations include but are not limited to, demosaicing, edge enhancement or sharpening, linear filtering, and non-linear filtering. A demosaicing operation is described herein, although the scheme is applied generally to spatial transformation operations. The spatial transformation methodology includes detailed expressions for the noise covariance after a spatial operation is performed for each of the three color channels, red, green, and blue. A color filter array is in the form of a Bayer pattern and demosaicing is performed using a 4-neighbor bilinear interpolation. Using lattice theory, the spatial transformation methodology predicts noise covariance after demosaicing in terms of the input noise covariance and an autocorrelation function of the image is determined for a given selectable number of shifts.

Abstract: A method and system are provided for approximating spectral sensitivities of a particular image sensor, the image sensor having a color filter array positioned over the image sensor. In one example of the method, the method involves measuring spectral sensitivities of a set of image sensors each having a color filter array positioned over the image sensor, calculating mean spectral sensitivities of the set of image sensors for each color within the color filter array, measuring outputs of a particular image sensor when capturing a picture of a plurality of color patches under a first illuminant and calculating spectral sensitivities of the particular image sensor using the mean spectral sensitivities and the output of the particular image sensor. In some embodiments, the method further comprises utilizing the calculated spectral sensitivities to determine outputs of the particular image sensor under a second illuminant.