Abstract: The present disclosure generally relates to camera management. In some embodiments, methods, computer systems, user interface, and techniques are provided for managing the resolution of media, managing zoom controls for capturing media, managing predefined zoom levels for capturing media, and managing media stabilization.

Abstract: Electronic devices, methods, and program storage devices for leveraging machine learning to perform high-resolution and low latency image fusion and/or noise reduction are disclosed. An incoming image stream may be obtained from an image capture device, wherein the incoming image stream comprises a variety of differently-exposed captures, e.g., EV0 images, EV? images, EV+ images, long exposure images, EV0/EV? image pairs, etc., which are received according to a particular pattern. When a capture request is received, two or more intermediate assets may be generated from images from the incoming image stream and fed into a neural network that has been trained to fuse and/or noise reduce the intermediate assets. In some embodiments, the resultant fused image generated from the two or more intermediate assets may have a higher resolution than at least one of the images that were used to generate at least one of the two or more intermediate assets.

Abstract: Techniques are disclosed for managing image capture and processing in a multi-camera imaging system. In such a system, a pair of cameras each may output a sequence of frames representing captured image data. The cameras' output may be synchronized to each other to cause synchronism in the image capture operations of the cameras. The system may assess image quality of frames output from the cameras and, based on the image quality, designate a pair of the frames to serve as a “reference frame pair.” Thus, one frame from the first camera and a paired frame from the second camera will be designated as the reference frame pair. The system may adjust each reference frame in the pair using other frames from their respective cameras. The reference frames also may be processed by other operations within the system, such as image fusion.

Abstract: Devices, methods, and computer-readable media are disclosed describing an adaptive approach for image bracket selection and fusion, e.g., to generate low noise and high dynamic range (HDR) images in a wide variety of capturing conditions. An incoming image stream may be obtained from an image capture device, wherein the incoming image stream comprises a variety of differently-exposed captures, e.g., EV0 images, EV? images, EV+ images, long exposure (or synthetic long exposure) images, EV0/EV? image pairs, etc., which are received according to a particular pattern. When a capture request is received, a set of rules and/or a decision tree may be used to evaluate one or more capture conditions associated with the images from the incoming image stream and determine which two or more images to select for a fusion operation. A noise reduction process may optionally be performed on the selected images before (or after) the registration and fusion operations.

Abstract: The disclosure pertains to techniques for image processing. One such technique comprises a method for image selection, comprising obtaining a sequence of images, detecting one or more objects in one or more images of the sequence of images, determining a location for each detected object in the one or more images, determining a trajectory for each detected object based on a determined location for each respective detected object in two or more images of the sequence of images, determining a trajectory waypoint score for the trajectory of each detected object, determining a set of selected images based on an aggregation of trajectory waypoint scores for each detected object in each respective image, and outputting the set of selected images for presentation.

Abstract: Electronic devices, methods, and program storage devices for leveraging machine learning to perform improved image fusion and/or noise reduction are disclosed. An incoming image stream may be obtained from an image capture device, wherein the incoming image stream comprises a variety of differently-exposed captures, e.g., EV0 images, EV? images, EV+ images, long exposure images, EV0/EV? image pairs, etc., which are received according to a particular pattern. When a capture request is received, two or more intermediate assets may be generated based on determined combinations of images from the incoming image stream, and the intermediate assets may then be fed into a neural network that has been trained to determine one or more sets of parameters to optimally fuse and/or noise reduce the intermediate assets. In some embodiments, the network may be trained to operate on levels of pyramidal decompositions of the intermediate assets independently, for increased efficiency and memory utilization.

Abstract: Devices, methods, and computer-readable media are disclosed describing an adaptive approach for image bracket selection and fusion, e.g., to generate low noise and high dynamic range (HDR) images in a wide variety of capturing conditions. An incoming image stream may be obtained from an image capture device, wherein the incoming image stream comprises a variety of differently-exposed captures, e.g., EV0 images, EV? images, EV+ images, long exposure (or synthetic long exposure) images, EV0/EV? image pairs, etc., which are received according to a particular pattern. When a capture request is received, a set of rules and/or a decision tree may be used to evaluate one or more capture conditions associated with the images from the incoming image stream and determine which two or more images to select for a fusion operation. A noise reduction process may optionally be performed on the selected images before (or after) the registration and fusion operations.

Abstract: Devices, methods, and computer-readable media are disclosed describing an adaptive approach for image bracket selection and fusion, e.g., to generate low noise and high dynamic range (HDR) images in a wide variety of capturing conditions. An incoming image stream may be obtained from an image capture device, wherein the incoming image stream comprises a variety of differently-exposed captures, e.g., EV0 images, EV? images, EV+ images, long exposure (or synthetic long exposure) images, EV0/EV? image pairs, etc., which are received according to a particular pattern. When a capture request is received, a set of rules and/or a decision tree may be used to evaluate one or more capture conditions associated with the images from the incoming image stream and determine which two or more images to select for a fusion operation. A noise reduction process may optionally be performed on the selected images before (or after) the registration and fusion operations.

Abstract: Disclosed herein is an apparatus. The apparatus includes a housing, electronic circuitry, and an audio-visual source tracking system. The electronic circuitry is in the housing. The audio-visual source tracking system includes a first video camera and an array of microphones. The first video camera and the array of microphones are attached to the housing. The audio-visual source tracking system is configured to receive video information from the first video camera. The audio-visual source tracking system is configured to capture audio information from the array of microphones at least partially in response to the video information. The audio-visual source tracking system might include a second video camera that is attached to the housing, wherein the first and second video cameras together estimate the beam orientation of the array of microphones.

Abstract: Techniques are disclosed for managing image capture and processing in a multi-camera imaging system. In such a system, a pair of cameras each may output a sequence of frames representing captured image data. The cameras' output may be synchronized to each other to cause synchronism in the image capture operations of the cameras. The system may assess image quality of frames output from the cameras and, based on the image quality, designate a pair of the frames to serve as a “reference frame pair.” Thus, one frame from the first camera and a paired frame from the second camera will be designated as the reference frame pair. The system may adjust each reference frame in the pair using other frames from their respective cameras. The reference frames also may be processed by other operations within the system, such as image fusion.

Abstract: Techniques are disclosed for managing image capture and processing in a multi-camera imaging system. In such a system, a pair of cameras each may output a sequence of frames representing captured image data. The cameras' output may be synchronized to each other to cause synchronism in the image capture operations of the cameras. The system may assess image quality of frames output from the cameras and, based on the image quality, designate a pair of the frames to serve as a “reference frame pair.” Thus, one frame from the first camera and a paired frame from the second camera will be designated as the reference frame pair. The system may adjust each reference frame in the pair using other frames from their respective cameras. The reference frames also may be processed by other operations within the system, such as image fusion.

Abstract: Devices, methods, and computer-readable media are disclosed describing an adaptive approach for image bracket selection and fusion, e.g., to generate low noise and high dynamic range (HDR) images in a wide variety of capturing conditions. An incoming image stream may be obtained from an image capture device, wherein the incoming image stream comprises a variety of differently-exposed captures, e.g., EV0 images, EV? images, EV+ images, long exposure (or synthetic long exposure) images, EV0/EV? image pairs, etc., which are received according to a particular pattern. When a capture request is received, a set of rules and/or a decision tree may be used to evaluate one or more capture conditions associated with the images from the incoming image stream and determine which two or more images to select for a fusion operation. A noise reduction process may optionally be performed on the selected images before (or after) the registration and fusion operations.

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 adaptively selecting what portion (aka slice) of a first image (aka frame) is selected to overlap and blend with a second frame during frame capture operations are disclosed. In general, for every new frame captured in a sequence the overlap between it and the slice selected from a prior frame may be determined based, at least in part, on sensor output. If the overlap so determined is below a desired threshold, the position of the current frame's slice may be adjusted so as to provide the desired overlap.

Abstract: Foreign lighting effects, such as lens flare, are very common in natural images. In a two-camera-system, the two images may be fused together to generate one image of a better quality. However, there are frequently different foreign light patterns in the two images that form the image pair, e.g., due to the difference in lens design, sensor and position, etc. Directly fusing such pairs of images will result in non-photorealistic images, with composed foreign light patterns from both images from the image pair. This disclosure describes a general foreign light mitigation scheme to detect all kinds of foreign light region mismatches. The detected foreign light mismatch regions may be deemphasized or excluded in the fusion step, in order to create a fused image that keeps a natural-looking foreign light pattern that is close to what was seen by the user of an image capture device during an image capture preview mode.

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: 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 fusion techniques hide artifacts that can arise at seams between regions of different image quality. According to these techniques, image registration may be performed on multiple images having at least a portion of image content in common. A first image may be warped to a spatial domain of a second image based on the image registration. A fused image may be generated from a blend of the warped first image and the second image, wherein relative contributions of the warped first image and the second image are weighted according to a distribution pattern based on a size of a smaller of the pair of images. In this manner, contributions of the different images vary at seams that otherwise would appear.