Abstract: A 9 pixel-by-9 pixel working window slides over an input Bayer image. For each such window, a demosaicing operation is performed. For each such window, corrective processing is performed relative to that window to produce relative differences for that window. For each such window for which relative differences have been produced, those relative differences are regulated. For each window, a maximum is found for that window's regulated relative differences; in one embodiment of the invention, this maximum is used to select which channel is sharp. For each window, the colors in that window are corrected based on the relative difference-based maximum found for that window. For each window, edge oversharpening is softened in order to avoid artifacts in the output image. The result is an output image in which axial chromatic aberrations have been corrected.

Abstract: A lens system is provided that comprises an enhanced depth of field based on a uniform or near uniform relative illumination via a lens with a large distortion. The distortion can be corrected with image processing equipment. The lens system can comprise an aperture stop and a group of lens, wherein there can be about five lenses in the group of lenses. The lens system is designed for relative illumination such that the light distribution over the lens system is substantially uniform.

Abstract: A technique of enhancing a scene containing one or more off-center peripheral regions within an initial distorted image captured with a large field of view includes determining and extracting an off-center region of interest (hereinafter “ROI”) within the image. Geometric correction is applied to reconstruct the off-center ROI into a rectangular frame of reference as a reconstructed ROI. A quality of reconstructed pixels is determined within the reconstructed ROI. Image analysis is selectively applied to the reconstructed ROI based on the quality of the reconstructed pixels.

Abstract: A technique of enhancing a scene containing one or more off-center peripheral regions within an initial distorted image captured with a large field of view includes determining and extracting an off-center region of interest (hereinafter “ROI”) within the image. Geometric correction is applied to reconstruct the off-center ROI into a rectangular or otherwise undistorted or less distorted frame of reference as a reconstructed ROI.

Abstract: A technique of enhancing a scene containing one or more off-center peripheral regions within an initial distorted image captured with a large field of view includes determining and extracting an off-center region of interest (hereinafter “ROI”) within the image. Geometric correction is applied to reconstruct the off-center ROI into a rectangular or otherwise undistorted or less distorted frame of reference as a reconstructed ROI. A quality of reconstructed pixels is determined within the reconstructed ROI. One or more additional initially distorted images is/are acquired, and matching additional ROIs are extracted and reconstructed to combine with reduced quality pixels of the first reconstructed ROI using a super-resolution technique to provide one or more enhanced ROIs.

Abstract: A technique of enhancing a scene containing one or more off-center peripheral regions within an initial distorted image captured with a large field of view includes determining and extracting an off-center region of interest (hereinafter “ROI”) within the image. Geometric correction is applied to reconstruct the off-center ROI into a rectangular or otherwise undistorted or less distorted frame of reference as a reconstructed ROI.

Abstract: A technique of enhancing a scene containing one or more off-center peripheral regions within an initial distorted image captured with a large field of view includes determining and extracting an off-center region of interest (hereinafter “ROI”) within the image. Geometric correction is applied to reconstruct the off-center ROI into a rectangular or otherwise undistorted or less distorted frame of reference as a reconstructed ROI. A quality of reconstructed pixels is determined within the constructed ROI. Image analysis is selectively applied to the reconstructed ROI based on the quality of the reconstructed pixels.

Abstract: In accordance with an embodiment of the invention, an anisotropic denoising method is provided that removes sensor noise from a digital image while retaining edges, lines, and details in the image. In one embodiment, the method removes noise from a pixel of interest based on the detected type of image environment in which the pixel is situated. If the pixel is situated in an edge/line image environment, then denoising of the pixel is increased such that relatively stronger denoising of the pixel occurs along the edge or line feature. If the pixel is situated in a detail image environment, then denoising of the pixel is decreased such that relatively less denoising of the pixel occurs so as to preserve the details in the image. In one embodiment, detection of the type of image environment is accomplished by performing simple arithmetic operations using only pixels in a 9 pixel by 9 pixel matrix of pixels in which the pixel of interest is situated.

Abstract: A method of detecting and applying a vertical gaze direction of a face within a digital image includes analyzing one or both eyes of a face within an acquired image, including determining a degree of coverage of an eye ball by an eye lid within the digital image. Based on the determined degree of coverage of the eye ball by the eye lid, an approximate direction of vertical eye gaze is determined. A further action is selected based on the determined approximate direction of vertical eye gaze.

Abstract: A measure of frame-to-frame rotation is determined. A global XY alignment of a pair of frames is performed. Local XY alignments in at least two matching corner regions of the pair of images are determined after the global XY alignment. Based on differences between the local XY alignments, a global rotation is determined between the pair of frames.

Abstract: A measure of frame-to-frame rotation is determined. Integral projection vector gradients are determined and normalized for a pair of images. Locations of primary maximum and minimum peaks of the integral projection vector gradients are determined. Based on normalized distances between the primary maximum and minimum peaks, a global image rotation is determined.

Abstract: A measure of frame-to-frame rotation is determined. A global XY alignment of a pair of image frames is performed. At least one section of each of the X and Y integral projection vectors is determined, where aligned global vectors demonstrate a significant localized difference. Based on X and Y locations of the at least one section of the X and Y integral projection vectors, location, relative velocity and/or approximate area of at least one moving object within the sequence of image frames is/are determined.

Abstract: Providing for optical imaging lenses employing non-linear distortion for image magnification is described herein. By way of example, a five-lens system can perform magnification functions by producing distortion that expands the image in the center of the field of view and compresses the image in the periphery of the field of view. This distortion can be corrected through image processing to yield a rectified image. Further, the five-lens system can be implemented as part of a fixed-focus imaging system, or in conjunction with a variable focus optical magnification imaging system.

Abstract: An autofocus method includes acquiring multiple images each having a camera lens focused at a different focus distance. A sharpest image is determined among the multiple images. Horizontal, vertical and/or diagonal integral projection (IP) vectors are computed for each of the multiple images. One or more IP vectors of the sharpest image is/are convoluted with multiple filters of different lengths to generate one or more filtered IP vectors for the sharpest image. Differences are computed between the one or more filtered IP vectors of the sharpest image and one or more IP vectors of at least one of the other images of the multiple images. At least one blur width is estimated between the sharpest image and the at least one of the other images of the multiple images as a minimum value among the computed differences over a selected range. The steps are repeated one or more times to obtain a sequence of estimated blur width values. A focus position is adjusted based on the sequence of estimated blur width values.

Abstract: An electronic device including a camera adapted to capture images is provided. The camera includes a lens having a first focusing state and a second focusing state; an image sensor adapted to capture a first image of an object in the first focusing state and a second image of the object in the second focusing state. The electronic device further includes a memory configured to store instructions; and a processor coupled to the memory and the camera, wherein the processor is adapted to determine a distance to the object based on the first image and the second image. A lens system including an actuator to modify a configuration of the lens system is also provided, having a first and second focusing state; a memory; and a lookup table to estimate a distance from the lens system to an object.

Abstract: Methods and systems for detecting and correcting chromatic aberration and purple fringing are disclosed. Chromatic aberration can be addressed by separating an image into color planes and then adjusting these to reduce chromatic aberration by using a specific calibration image (calibration chart) as an empirical method to calibrate the image acquisition device. Purple fringing can be corrected by initially addressing color aberration resulting from the lateral chromatic aberration (LCA). The LCA is first removed and then the correction is extended to purple fringing. A discovery is relied upon that the purple fringing is created in the direction of the chromatic aberration and is more pronounced in the direction of the chromatic aberration.

Abstract: A smart-focusing technique includes identifying an object of interest, such as a face, in a digital image. A focus-generic classifier chain is applied that is trained to match both focused and unfocused faces and/or data from a face tracking module is accepted. Multiple focus-specific classifier chains are applied, including a first chain trained to match substantially out of focus faces, and a second chain trained to match slightly out of focus faces. Focus position is rapidly adjusted using a MEMS component.

Abstract: An apparatus for the remote wireless control of a consumer electronic audio and/or visual (A/V) appliance such as a TV set, and/or for internet uploading, includes a remote control handset that includes an A/V device component such as a digital still and/or video camera and/or audio player/recorder, and is configured to one or both of directly control the CE appliance, or indirectly via a wireless receiver, for playing, displaying, transitioning and/or editing audio and/or visual data, including a push-button image processing interface including multiple push-buttons configured to be depressible by one or more fingers or a thumb or both of a same hand within which the remote control handset is being held by a user.

Abstract: A smart-focusing technique includes identifying an object of interest, such as a face, in a digital image. A focus-generic classifier chain is applied that is trained to match both focused and unfocused faces and/or data from a face tracking module is accepted. Multiple focus-specific classifier chains are applied, including a first chain trained to match substantially out of focus faces, and a second chain trained to match slightly out of focus faces. Focus position is rapidly adjusted using a MEMS component.

Abstract: An autofocus method includes acquiring an image of a scene that includes one or more out of focus faces and/or partial faces. The method includes detecting one or more of the out of focus faces and/or partial faces within the digital image by applying one or more sets of classifiers trained on faces that are out of focus. One or more sizes of the one of more respective out of focus faces and/or partial faces is/are determined within the digital image. One or more respective depths is/are determined to the one or more out of focus faces and/or partial faces based on the one or more sizes of the one of more faces and/or partial faces within the digital image. One or more respective focus positions of the lens is/are adjusted to focus approximately at the determined one or more respective depths.