Apple Inc.

Nvidia Corporation

Jun Hu

Marius Tico

Kari Pulli

Orazio Gallo

Hao Sun

Wei Sun

Alok Deshpande

Wu Cheng

Anita Nariani Schulze

Morten Poulsen

Tak Shing Wong

Justin Michael Faryabi-Allen

Xuemei Zhang

Tongbo Chen

Tiffany Jou Cheng

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&#x2212; images, EV+ images, long exposure (or synthetic long exposure) images, EV0/EV&#x2212; 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.

Unified bracketing approach for imaging

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.

Motion trajectory tracking for action detection

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&#x2212; images, EV+ images, long exposure images, EV0/EV&#x2212; 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.

Deep learning-based image fusion for noise reduction and high dynamic range

Adaptive focus sweep (AFS) techniques for image processing are described. For one technique, an AFS logic/module can obtain an AFS representing a scene, where the AFS is a sequence of images representing the scene that includes: (i) a first image representing the scene captured at a first focus position; and (ii) a second image representing the scene captured at a second focus position that differs from the first focus position. The first focus position can be associated with a first depth of field (DOField) that is determined based on an autofocus technique. The second focus position can be associated with a second DOField, where the second focus position is at least two DOFields away from the first focus position. The AFS logic/module can detect a foreground of the scene in the first image based on information acquired from the first and second images. Other embodiments are described.

Adaptive focus sweep techniques for foreground/background separation

A system, method, and computer program product are provided for performing fast, non-rigid registration for at least two images of a high-dynamic range image stack. The method includes the steps of generating a warped image based on a set of corresponding pixels, analyzing the warped image to detect unreliable pixels in the warped image, and generating a corrected pixel value for each unreliable pixel in the warped image. The set of corresponding pixels includes a plurality of pixels in a source image, each pixel in the plurality of pixels associated with a potential feature in the source image and paired with a corresponding pixel in a reference image that substantially matches the pixel in the source image.

System, method, and computer program product for performing fast, non-rigid registration for high dynamic range image stacks

A set of images is processed to modify and register the images to a reference image in preparation for blending the images to create a high-dynamic range image. To modify and register a source image to a reference image, a processing unit generates correspondence information for the source image based on a global correspondence algorithm, generates a warped source image based on the correspondence information, estimates one or more color transfer functions for the source image, and fills the holes in the warped source image. The holes in the warped source image are filled based on either a rigid transformation of a corresponding region of the source image or a transformation of the reference image based on the color transfer functions.

Techniques for registering and warping image stacks

A set of images is processed to modify and register the images to a reference image in preparation for blending the images to create a high-dynamic range image. To modify and register a source image to a reference image, a processing unit generates a correspondence map for the source image based on a non-rigid dense correspondence algorithm, generates a warped source image based on the correspondence map, estimates one or more color transfer functions for the source image, and fills the holes in the warped source image. The holes in the warped source image are filled based on either a rigid transformation of a corresponding region of the source image or a transformation of the reference image based on the color transfer functions.

Growing community of inventors

Sunnyvale, CA, United States of America

Jun Hu