Abstract: Systems and methods for calibrating an array camera are disclosed. Systems and methods for calibrating an array camera in accordance with embodiments of this invention include the capturing of an image of a test pattern with the array camera such that each imaging component in the array camera captures an image of the test pattern. The image of the test pattern captured by a reference imaging component is then used to derive calibration information for the reference component. A corrected image of the test pattern for the reference component is then generated from the calibration information and the image of the test pattern captured by the reference imaging component. The corrected image is then used with the images captured by each of the associate imaging components associated with the reference component to generate calibration information for the associate imaging components.

Abstract: In an embodiment, a 3D facial modeling system includes a plurality of cameras configured to capture images from different viewpoints, a processor, and a memory containing a 3D facial modeling application and parameters defining a face detector, wherein the 3D facial modeling application directs the processor to obtain a plurality of images of a face captured from different viewpoints using the plurality of cameras, locate a face within each of the plurality of images using the face detector, wherein the face detector labels key feature points on the located face within each of the plurality of images, determine disparity between corresponding key feature points of located faces within the plurality of images, and generate a 3D model of the face using the depth of the key feature points.

Abstract: Systems and methods are described for generating, using the first trained machine learning model and based on text input, a single-layer image comprising a plurality of objects; generating a plurality of masks associated with the plurality of objects; determining a plurality of attributes associated with the plurality of objects; generating, using a second trained machine learning model, a plurality of textual descriptions respectively corresponding to the plurality of objects; inputting the plurality of textual descriptions, and the plurality of attributes, to the first trained machine learning model; generating, using the first trained machine learning model, a plurality of images respectively corresponding to the plurality of textual descriptions; and generating the multi-layer image by combining the plurality of images and by using the plurality of masks, wherein the plurality of images respectively correspond to a plurality of layers of the multi-layer image.

Abstract: Systems and methods for estimating depth from projected texture using camera arrays are described. A camera array includes a conventional camera and at least one two-dimensional array of cameras, where the conventional camera has a higher resolution than the cameras in the at least one two-dimensional array of cameras, an illumination system configured to illuminate a scene with a projected texture, where an image processing pipeline application directs the processor to: utilize the illumination system controller application to control the illumination system to illuminate a scene with a projected texture, capture a set of images of the scene illuminated with the projected texture, and determining depth estimates for pixel locations in an image from a reference viewpoint using at least a subset of the set of images.

Abstract: Methods, systems, and devices are described herein for providing an extended reality (XR) experience capable of both augmented reality (AR) and virtual reality (VR) functions. The XR device comprises transparent components enabling optical see-through (OST); the device comprising a transparent microLED display and a transparent image sensor together with a meta-lens. The XR OST device can switch between a fully immersive VR mode and an AR mode by controlling the display's transparency. Light from the exterior real-world scene is collected by the meta-lens on a wide field of view (FOV) and focused onto the transparent image sensor to detect objects in the real-world environment.

Abstract: The present disclosure is directed to systems and methods to enhance the process of creating an artificial intelligence (AI) generated content or content items, such as images, text, video, sounds, etc., using a text or other suitable prompt, such as via voice input. The systems and methods disclosed provide streamlined content generation with, e.g., reduced processing power and computing time. In an embodiment the systems and methods receive a prompt for generating a first content item using a generative artificial intelligence (AI) model and retrieve, based on the prompt, a collection of matching content items. The systems and methods may then receive input selecting one of the content items from the collection and identify a prompt used to generate the selected content item. The systems and methods may then merge using a trained natural language processing model, the received prompt with the prompt of the selected content item to create a third prompt.

Abstract: Systems and methods are described for correcting rolling shutter effect for images captured by a camera. In some embodiments, an adjustable transparency layer is placed between an image sensor and a camera lens, where the adjustable transparency layer is controlled to become transparent and then opaque in order to produce a single concurrent effective exposure time, reducing rolling shutter effect.

Abstract: Systems and methods for hybrid depth regularization in accordance with various embodiments of the invention are disclosed. In one embodiment of the invention, a depth sensing system comprises a plurality of cameras; a processor; and a memory containing an image processing application. The image processing application may direct the processor to obtain image data for a plurality of images from multiple viewpoints, the image data comprising a reference image and at least one alternate view image; generate a raw depth map using a first depth estimation process, and a confidence map; and generate a regularized depth map. The regularized depth map may be generated by computing a secondary depth map using a second different depth estimation process; and computing a composite depth map by selecting depth estimates from the raw depth map and the secondary depth map based on the confidence map.

Abstract: Systems and methods for implementing array cameras configured to perform super-resolution processing to generate higher resolution super-resolved images using a plurality of captured images and lens stack arrays that can be utilized in array cameras are disclosed. An imaging device in accordance with one embodiment of the invention includes at least one imager array, and each imager in the array comprises a plurality of light sensing elements and a lens stack including at least one lens surface, where the lens stack is configured to form an image on the light sensing elements, control circuitry configured to capture images formed on the light sensing elements of each of the imagers, and a super-resolution processing module configured to generate at least one higher resolution super-resolved image using a plurality of the captured images.

Abstract: Systems and methods are provided for improving image item editing. An image item is selected at a computing device and with an editing application, and a preferred editing option to apply to the image item is identified via a user profile. The preferred editing option is determined based on historic editing actions for a plurality of different image items. An icon for applying the preferred editing option to the image item is generated for display in a user interface of the editing application. User input associated with the icon is received, and the preferred editing option is applied to the image item.

Abstract: Systems and methods for calibrating an array camera are disclosed. Systems and methods for calibrating an array camera in accordance with embodiments of this invention include the capturing of an image of a test pattern with the array camera such that each imaging component in the array camera captures an image of the test pattern. The image of the test pattern captured by a reference imaging component is then used to derive calibration information for the reference component. A corrected image of the test pattern for the reference component is then generated from the calibration information and the image of the test pattern captured by the reference imaging component. The corrected image is then used with the images captured by each of the associate imaging components associated with the reference component to generate calibration information for the associate imaging components.

Abstract: In an embodiment, a 3D facial modeling system includes a plurality of cameras configured to capture images from different viewpoints, a processor, and a memory containing a 3D facial modeling application and parameters defining a face detector, wherein the 3D facial modeling application directs the processor to obtain a plurality of images of a face captured from different viewpoints using the plurality of cameras, locate a face within each of the plurality of images using the face detector, wherein the face detector labels key feature points on the located face within each of the plurality of images, determine disparity between corresponding key feature points of located faces within the plurality of images, and generate a 3D model of the face using the depth of the key feature points.

Abstract: The claims generally describe an apparatus for spatial image and video capture comprising a micro-electromechanical Systems (MEMS) scanning mirror. The apparatus comprises an image sensor with a MEMS scanning mirror positioned centrally in front of the sensor that rapidly alternate the optical pass. The MEMS mirror directs light received through a first light-permeable element and reflected by a first mirror to the sensor while it is oriented in a first position, and a first image is captured based on light reflected by the first mirror and directed to the sensor. The MEMS mirror directs light received by a second light-permeable element and reflected by the second mirror to the sensor while it is oriented in a second position, and a second image is captured based on light reflected by the second mirror and directed to the sensor. A spatial video is generated based on at least the first and second captured images.

Abstract: System and method are provided for capturing images based on dominant eye characteristics of a user. The system detects, by a wearable device, a hand gesture of the user indicating an image boundary for capturing an image from one or more cameras of the wearable device. The system generates, by the wearable device, the image based on (a) a dominant eye characteristic of the user, (b) the image boundary indicated by the hand gesture of the user, and (c) one or more images captured from the one or more cameras of the wearable device. The system stores, by the wearable device, the generated image to memory.

Abstract: A digital camera and a dual-mode viewfinder are provided. The camera includes aligned components along a common axis for real-time optical viewing, quick subject capture, and digital overlay display without switching modes or viewpoints. Features include a real-time display, an optical viewfinder (OVF) experience, a graphene-based sensor, a compact design, a common axis OVF/digital viewfinder (DVF) design, dual-mode functionality, single viewpoint operation, a transparent display, a real-time overlay, automatic switching, user-triggered image capture, one or more coatings, and battery life optimization. The dual-mode viewfinder includes a transparent imaging sensor and a transparent display, both aligned along a common axis. It can switch between an optical viewfinder mode and a digital viewfinder mode based on a user command or a proximity sensor detecting a distance to a user. The system also includes control circuitry configured to perform these features.

Abstract: A hand-held digital camera has a touch-sensitive display screen (“touch screen”) for image preview and user control of the camera, and a user-selectable panorama mode. Upon entering panorama mode the camera superimposes upon the touch screen a horizontal rectangular bar whose width and/or height are user-adjustable by interaction with the touch screen to select a desired horizontal sweep angle. After the sweep angle is set the camera automatically captures successive horizontally overlapping images during a sweep of the device through the selected sweep angle. Subsequently the camera synthesises a panoramic image from the successively captured images, the panoramic image having a width corresponding to the selected sweep angle.

Abstract: Systems and methods for implementing array cameras configured to perform super-resolution processing to generate higher resolution super-resolved images using a plurality of captured images and lens stack arrays that can be utilized in array cameras are disclosed. An imaging device in accordance with one embodiment of the invention includes at least one imager array, and each imager in the array comprises a plurality of light sensing elements and a lens stack including at least one lens surface, where the lens stack is configured to form an image on the light sensing elements, control circuitry configured to capture images formed on the light sensing elements of each of the imagers, and a super-resolution processing module configured to generate at least one higher resolution super-resolved image using a plurality of the captured images.

Abstract: Systems and methods are described for automatically performing automated radial blurring. A plurality of images may be accessed, and saliency parameters may be determined based on at least one of the plurality of images. Co-saliency parameters may be determined based on the plurality of images. A region of interest (ROI) in the at least one of the plurality of images may be determined based on the saliency parameters and the co-saliency parameters. Automated radial blurring of the at least one of the plurality of images may be performed based on the identified ROI.

Abstract: A hand-held digital camera has a touch-sensitive display screen (“touch screen”) for image preview and user control of the camera, and a user-selectable panorama mode. Upon entering panorama mode the camera superimposes upon the touch screen a horizontal rectangular bar whose width and/or height are user-adjustable by interaction with the touch screen to select a desired horizontal sweep angle. After the sweep angle is set the camera automatically captures successive horizontally overlapping images during a sweep of the device through the selected sweep angle. Subsequently the camera synthesises a panoramic image from the successively captured images, the panoramic image having a width corresponding to the selected sweep angle.

Abstract: Systems and methods for dynamically calibrating an array camera to accommodate variations in geometry that can occur throughout its operational life are disclosed. The dynamic calibration processes can include acquiring a set of images of a scene and identifying corresponding features within the images. Geometric calibration data can be used to rectify the images and determine residual vectors for the geometric calibration data at locations where corresponding features are observed. The residual vectors can then be used to determine updated geometric calibration data for the camera array. In several embodiments, the residual vectors are used to generate a residual vector calibration data field that updates the geometric calibration data. In many embodiments, the residual vectors are used to select a set of geometric calibration from amongst a number of different sets of geometric calibration data that is the best fit for the current geometry of the camera array.