Abstract: Techniques to capture and fuse short- and long-exposure images of a scene from a stabilized image capture device are disclosed. More particularly, the disclosed techniques use not only individual pixel differences between co-captured short- and long-exposure images, but also the spatial structure of occluded regions in the long-exposure images (e.g., areas of the long-exposure image(s) exhibiting blur due to scene object motion). A novel device used to represent this feature of the long-exposure image is a “spatial difference map.” Spatial difference maps may be used to identify pixels in the short- and long-exposure images for fusion and, in one embodiment, may be used to identify pixels from the short-exposure image(s) to filter post-fusion so as to reduce visual discontinuities in the output image.

Abstract: Techniques to capture and fuse short- and long-exposure images of a scene from a stabilized image capture device are disclosed. More particularly, the disclosed techniques use not only individual pixel differences between co-captured short- and long-exposure images, but also the spatial structure of occluded regions in the long-exposure images (e.g., areas of the long-exposure image(s) exhibiting blur due to scene object motion). A novel device used to represent this feature of the long-exposure image is a “spatial difference map.” Spatial difference maps may be used to identify pixels in the short-and long-exposure images for fusion and, in one embodiment, may be used to identify pixels from the short-exposure image(s) to filter post-fusion so as to reduce visual discontinuities in the output image.

Abstract: Systems, methods, and computer readable media for calibrating two cameras (image capture units) using a non-standard, and initially unknown, calibration object are described. More particularly, an iterative approach to determine the structure and pose of an target object in an unconstrained environment are disclosed. The target object may be any of a number of predetermined objects such as a specific three dimensional (3D) shape, a specific type of animal (e.g., dogs), or the face of an arbitrary human. Virtually any object whose structure may be expressed in terms of a relatively low dimensional parametrized model may be used as a target object. The identified object (i.e., its pose and shape) may be used as input to a bundle adjustment operation resulting in camera calibration.

Abstract: Pixel binning is performed by summing charge from some pixels positioned diagonally in a pixel array. Pixel signals output from pixels positioned diagonally in the pixel array may be combined on the output lines. A signal representing summed charge produces a binned 2×1 cluster. A signal representing combined voltage signals produces a binned 2×1 cluster. A signal representing summed charge and a signal representing combined pixel signals can be combined digitally to produce a binned 2×2 pixel. Orthogonal binning may be performed on other pixels in the pixel array by summing charge on respective common sense regions and then then combining the voltage signals that represent the summed charge on respective output lines.

Abstract: Techniques to capture and fuse short- and long-exposure images of a scene from a stabilized image capture device are disclosed. More particularly, the disclosed techniques use not only individual pixel differences between co-captured short- and long-exposure images, but also the spatial structure of occluded regions in the long-exposure images (e.g., areas of the long-exposure image(s) exhibiting blur due to scene object motion). A novel device used to represent this feature of the long-exposure image is a “spatial difference map.” Spatial difference maps may be used to identify pixels in the short-and long-exposure images for fusion and, in one embodiment, may be used to identify pixels from the short-exposure image(s) to filter post-fusion so as to reduce visual discontinuities in the output image.

Abstract: Image enhancement is achieved by separating image signals, e.g. YCbCr image signals, into a series of frequency bands and performing locally-adaptive noise reduction on bands below a given frequency but not on bands above that frequency. The bands are summed to develop the image enhanced signals. The YCbCr, multi-band locally-adaptive approach to denoising is able to operate independently—and in an optimized fashion—on both luma and chroma channels. Noise reduction is done based on models developed for both luma and chroma channels by measurements taken for multiple frequency bands, in multiple patches on the ColorChecker chart, and at multiple gain levels, in order to develop a simple yet robust set of models that may be tuned off-line a single time for each camera and then applied to images taken by such cameras in real-time without excessive processing requirements and with satisfactory results across illuminant types and lighting conditions.

Abstract: Techniques for de-noising a digital image using a multi-band noise filter and a unique combination of texture and chroma metrics are described. A novel texture metric may be used during multi-band filter operations on an image's luma channel to determine if a given pixel is associated with a textured/smooth region of the image. A novel chroma metric may be used during the same multi-band filter operation to determine if the same pixel is associated with a blue/not-blue region of the image. Pixels identified as being associated with a smooth blue region may be aggressively de-noised and conservatively sharpened. Pixels identified as being associated with a textured blue region may be conservatively de-noised and aggressively sharpened. By coupling texture and chroma constraints it has been shown possible to mitigate noise in an image's smooth blue regions without affecting the edges/texture in other blue objects.

Abstract: Systems, methods, and computer readable media to fuse digital images are described. In general, techniques are disclosed that use multi-band noise reduction techniques to represent input and reference images as pyramids. Once decomposed in this manner, images may be fused using novel low-level (noise dependent) similarity measures. In some implementations similarity measures may be based on intra-level comparisons between reference and input images. In other implementations, similarity measures may be based on inter-level comparisons. In still other implementations, mid-level semantic features such as black-level may be used to inform the similarity measure. In yet other implementations, high-level semantic features such as color or a specified type of region (e.g., moving, stationary, or having a face or other specified shape) may be used to inform the similarity measure.

Abstract: An image sensor includes a pixel array having a plurality of pixels. Pixels can be summed or binned diagonally in the pixel array in a first diagonal direction and in a different second diagonal direction. The locations of the first and second diagonal summed pairs can be distributed across the pixel array.

Abstract: Generating an image with a selected level of background blur includes capturing, by a first image capture device, a plurality of frames of a scene, wherein each of the plurality of frames has a different focus depth, obtaining a depth map of the scene, determining a target object and a background in the scene based on the depth map, determining a goal blur for the background, and selecting, for each pixel in an output image, a corresponding pixel from the focus stack.

Abstract: Systems, methods, and computer readable media to fuse digital images are described. In general, techniques are disclosed that use multi-band noise reduction techniques to represent input and reference images as pyramids. Once decomposed in this manner, images may be fused using novel low-level (noise dependent) similarity measures. In some implementations similarity measures may be based on intra-level comparisons between reference and input images. In other implementations, similarity measures may be based on inter-level comparisons. In still other implementations, mid-level semantic features such as black-level may be used to inform the similarity measure. In yet other implementations, high-level semantic features such as color or a specified type of region (e.g., moving, stationary, or having a face or other specified shape) may be used to inform the similarity measure.

Abstract: Techniques for de-noising a digital image using a multi-band noise filter and a unique combination of texture and chroma metrics are described. A novel texture metric may be used during multi-band filter operations on an image's luma channel to determine if a given pixel is associated with a textured/smooth region of the image. A novel chroma metric may be used during the the same multi-band filter operation to determine if the same pixel is associated with a blue/not-blue region of the image. Pixels identified as being associated with a smooth blue region may be aggressively de-noised and conservatively sharpened. Pixels identified as being associated with a textured blue region may be conservatively de-noised and aggressively sharpened. By coupling texture and chroma constraints it has been shown possible to mitigate noise in an image's smooth blue regions without affecting the edges/texture in other blue objects.

Abstract: A method for generating a depth map is described. The method includes obtaining a first image of a scene from a first image capture unit, the first image having a first depth-of-field (DOF), obtaining a second image of the scene from a second image capture unit, the second image having a second DOF that is different than the first DOF. Each pixel in the second image has a corresponding pixel in the first image. The method also includes generating a plurality of third images, each corresponding to a blurred version of the second image at each of a plurality of specified depths, generating a plurality of fourth images, each representing a difference between the first image and one or the plurality of third images, and generating a depth map where each pixel in the depth map is based on the pixels in one of the plurality of fourth images.

Abstract: A method to improve the efficiency of the detection and tracking of machine-readable objects is disclosed. The properties of image frames may be pre-evaluated to determine whether a machine-readable object, even if present in the image frames, would be likely to be detected. After it is determined that one or more image frames have properties that may enable the detection of a machine-readable object, image data may be evaluated to detect the machine-readable object. When a machine-readable object is detected, the location of the machine-readable object in a subsequent frame may be determined based on a translation metric between the image frame in which the object was identified and the subsequent frame rather than a detection of the object in the subsequent frame. The translation metric may be identified based on an evaluation of image data and/or motion sensor data associated with the image frames.

Abstract: Systems and methods for improving automatic selection of keeper images from a commonly captured set of images are described. A combination of image type identification and image quality metrics may be used to identify one or more images in the set as keeper images. Image type identification may be used to categorize the captured images into, for example, three or more categories. The categories may include portrait, action, or “other.” Depending on the category identified, the images may be analyzed differently to identify keeper images. For portrait images, an operation may be used to identify the best set of faces. For action images, the set may be divided into sections such that keeper images selected from each section tell the story of the action. For the “other” category, the images may be analyzed such that those having higher quality metrics for an identified region of interest are selected.

Abstract: Image enhancement is achieved by separating image signals, e.g. YCbCr image signals, into a series of frequency bands and performing noise reduction on bands below a given frequency but not on bands above that frequency. The bands are summed to develop the image enhanced signals. The YCbCr, multi-band approach to denoising is able to operate independently—and in an optimized fashion—on both luma and chroma channels. Noise reduction is done based on models developed for both luma and chroma channels by measurements taken for multiple frequency bands, in multiple patches on the ColorChecker chart, and at multiple gain levels, in order to develop a simple yet robust set of models that may be tuned off-line a single time for each camera and then applied to images taken by such cameras in real-time without excessive processing requirements and with satisfactory results across illuminant types and lighting conditions.

Abstract: The invention relates to systems, methods, and computer readable media for responding to a user snapshot request by capturing anticipatory pre-snapshot image data as well as post-snapshot image data. The captured information may be used, depending upon the embodiment, to create archival image information and image presentation information that is both useful and pleasing to a user. The captured information may automatically be trimmed or edited to facilitate creating an enhanced image, such as a moving still image. Varying embodiments of the invention offer techniques for trimming and editing based upon the following: exposure, brightness, focus, white balance, detected motion of the camera, substantive image analysis, detected sound, image metadata, and/or any combination of the foregoing.

Abstract: Systems, methods, and computer readable media to improve image stabilization operations are described. A novel combination of image quality and commonality metrics are used to identify a reference frame from a set of commonly captured images which, when the set's other images are combined with it, results in a quality stabilized image. The disclosed image quality and commonality metrics may also be used to optimize the use of a limited amount of image buffer memory during image capture sequences that return more images that the memory may accommodate at one time. Image quality and commonality metrics may also be used to effect the combination of multiple relatively long-exposure images which, when combined with a one or more final (relatively) short-exposure images, yields images exhibiting motion-induced blurring in interesting and visually pleasing ways.

Abstract: Techniques to capture and fuse short- and long-exposure images of a scene from a stabilized image capture device are disclosed. More particularly, the disclosed techniques use not only individual pixel differences between co-captured short- and long-exposure images, but also the spatial structure of occluded regions in the long-exposure images (e.g., areas of the long-exposure image(s) exhibiting blur due to scene object motion). A novel device used to represent this feature of the long-exposure image is a “spatial difference map.” Spatial difference maps may be used to identify pixels in the short-and long-exposure images for fusion and, in one embodiment, may be used to identify pixels from the short-exposure image(s) to filter post-fusion so as to reduce visual discontinuities in the output image.

Abstract: For cameras that capture several images in a burst mode, some embodiments of the invention provide a method that presents one or more of the captured images differently than the remaining captured images. The method identifies at least one captured image as dominant image and at least another captured image as a non-dominant image. The method then displays each dominant image different from each non-dominant image in a concurrent presentation of the images captured during the burst mode. The dominant images may appear larger than non-dominant images, and/or appear with a marking that indicates that the images are dominant.