Apple Inc.

Light Labs Inc.

Maxoptix Corporation

Feng Li

Jianping Zhou

Saeed Izadi

Jia Xue

Rajiv Laroia

Florian Ciurea

Alan Claude Burroughs

Sebastien X Beysserie

Jannie Lai

Xiaoxing Li

Donald F Johann

Ahmed Tashrif Kamal

Stephane S Ben Soussan

Fan Sai Kuok

Jared Torres Calinisan

Libin Sun

Neil T Phipps

Devices, methods, and non-transitory computer readable media are disclosed herein to repair or mitigate the appearance of unwanted reflection artifacts in captured video image streams. These unwanted reflection artifacts often present themselves as brightly-colored spots, circles, rings, or halos that reflect the shape of a bright light source in the captured image. These artifacts, also referred to herein as 'ghosts' or “green ghosts” (due to often having a greenish tint), are typically located in regions of the captured images where there is not actually a bright light source located in the image. In fact, such unwanted reflection artifacts often present themselves on the image sensor across the principal point of the lens from where the actual bright light source in the captured image is located. Such devices, methods and computer readable media may be configured to detect, track, and repair such unwanted reflection artifacts in an intelligent and efficient fashion.

Efficient unwanted reflection artifact mitigation in videos for embedded devices

Devices, methods, and computer readable media to provide enhanced images in multi-camera systems, e.g., by using images captured by cameras with different optical properties and/or sensors. In one embodiment, the techniques comprise reference-based image super-resolution techniques for producing, with a first neural network employing robust feature aggregation techniques (e.g., techniques able to blend between single-image enhancement and feature aggregation, when appropriate), an enhanced output image that attempts to match the quality characteristics of each of region in a lower quality (e.g., shorter focal length, larger field of view (FOV)) input image with the quality characteristics of the region's determined guidance region from at least a second, i.e., higher quality (e.g., longer focal length, smaller FOV image) input image. The guidance regions from the higher quality image that are determined for each region from the lower quality input image may be determined by performing homographic mapping and/or semantic feature matching techniques.

Reference-based super-resolution for image and video enhancement

Devices, methods, and non-transitory program storage devices are disclosed to provide enhanced images in multi-camera systems, e.g., by using information from images captured by cameras with different properties in terms of optics and/or sensors. In one embodiment, the techniques comprise: obtaining a first image from a first image capture device, wherein the first image has a first field of view (FOV) and a first set of quality characteristics; obtaining a second image from a second image capture device, wherein the second image has a second FOV and a second set of quality characteristics, and wherein the second FOV partially overlaps the first FOV; obtaining a neural network that produces a modified second image having a modified second set of quality characteristics determined by the neural network attempting to match the first set of quality characteristics; and generating an output image based, at least in part, on the modified second image.

Systems and methods for multi-sensor image enhancement

A camera device including multiple optical chains receives user input indicating one or more user selectable control option setting(s), e.g., a user selected depth of field control option setting and an aspect ratio control option setting. The indicated control option settings are stored in memory in the camera device. Multiple optical chains of the camera device capture an image during an image capture time interval. A set of captured images are stored in a file along with metadata including the user indicated control option settings. A composite image is generated from a set of captured images in accordance with the stored indicated control option settings, e.g., generating a composite image with a user selected depth of field and/or a user selected aspect ratio. In some embodiments, at least some of the user selected control option settings are not used during image capture operations but are used in subsequent image processing.

Methods and apparatus for receiving and storing in a camera a user controllable setting that is used to control composite image generation performed after image capture

In an optical recording system employing a laser for writing digital data onto a storage medium, an apparatus and method for calibrating a laser to a power level which minimizes write errors. A calibration profile is generated by writing test data patterns onto the storage medium by using a number of different predetermined laser power levels. The test data patterns are then read back. The number of write errors is determined for each of the different laser power levels by comparing the written data patterns to the read back data patterns. The calibration profile denotes the number of write errors as a function of laser power levels. Next, an optimum laser power is determined corresponding to an average of an upper and a lower knee of the calibration profile. The laser is calibrated to this optimum power level.

Method and apparatus for calibrating laser write power by averaging

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Fremont, CA, United States of America

Feng Li