Systems and methods for identifying root causes for and/or correcting pointing error in moving platform imaging. Scene frames captured by a sensor (e.g., a focal plane array, etc.) are digitally transformed to compensate for relative motion between the scene and platform, then motion residuals are computed based on inter-frame scene gradients, and image eigenfunctions are fit to the motion residuals to compute coefficients that may be used to efficiently correct future image acquisition, determine root cause(s) of pointing (e.g., sensor pointing error, scene mean altitude, platform altitude, etc.) errors, and further digitally correct the captured images. Comparisons may be made to a database of residual transformation coefficients based on known or expected relative motion of the platform to the scene and a known or expected pointing angle. Truly moving targets may be identified, removed and re-added after image digital transformation processing.

An image processing device including a blurring processing unit that performs blurring processing for input image information that is input, includes: a saturation determination unit that calculates saturation of a saturated pixel of the input image information; a first gradation conversion unit that converts gradation of the input image information on a basis of the saturation calculated by the saturation determination unit; and, in which the blurring processing unit performs blurring processing for image information that has been subjected to gradation conversion by the first gradation conversion unit, a second gradation conversion unit that converts gradation of image information that has been subjected to the blurring processing, on a basis that gradation of a saturated region of the blurring-processed image information serves as a gradation of a saturated pixel.

A digital graduated filter is implemented in an imaging device by combining multiple images of the subject wherein the combining may include combining different numbers of images for highlights and for shadows of the subject. The imaging device may present a user with a set of pre-defined graduated filter configurations to choose from. A user may also specify the direction of graduation and strength of graduation in a viewfinder. In an alternative implementation, combining may include scaling of pixels being added instead of varying the number of images being combined. In an alternative implementation, the combining of multiple images may include combining a different number of images for highlights of the subject than for shadows of subject.

An image sequence includes consecutive video images each exhibiting at least one image region having a number of pixels, where each pixel includes at least one intensity value. For each image a motion measure value is determined indicative of temporal change of a video content of the image region and varying the intensity values of the pixels of the image region relative to the associated intensity values from video image to video image, a measure for the variation of the intensity values being dependent on the motion measure value determined and the change in the intensity values relative to the associated intensity values being greater the larger the motion represented by the motion measure value.

A cross reality system enables any of multiple types of devices to efficiently and accurately access previously stored maps and render virtual content specified in relation to those maps. The cross reality system may include a cloud-based localization service that responds to requests from devices to localize with respect to a stored map. Devices of any type, with native hardware and software configured for augmented reality operations may be configured to work with the cross reality system by incorporating components that interface between the native AR framework of the device and the cloud-based localization service. These components may present position information about the device in a format recognized by the localization service. Additionally, these components may filter or otherwise process perception data provided by the native AR framework to increase the accuracy of localization.

A method for deblurring a target image includes receiving a burst of images that includes the target image; partitioning respective images of the burst of images into respective patches; converting, to a frequency domain, the respective patches into respective transform patches; selecting a first set of corresponding transform patches from the respective transform patches, where the first set of the corresponding transform patches includes a respective transform patch for a respective image of the burst of images; obtaining, using a neural network, respective weight maps for the corresponding transform patches; obtaining a deblurred transform patch by combining the first set of corresponding transform patches using the respective weight maps; obtaining a first deblurred patch by converting the deblurred transform patch to a pixel domain; and obtaining a deblurred image of the target image using the first deblurred patch.

A method and apparatus for capturing digital video includes displaying a preview of a field of view of the imaging device in a user interface of the imaging device. A sequence of images is captured. A main subject and a background in the sequence of images is determined, wherein the main subject is different than the background. A sequence of modified images for use in a final video is obtained, wherein each modified image is obtained by combining two or more images of the sequence of images such that the main subject in the modified image is blur free and the background is blurred. The sequence of modified images is combined to obtain the final video, which is stored in a memory of the imaging device, and displayed in the user interface.

A processing device which obtains distance information of a subject, including: a calculation unit configured to calculate the distance information of the subject from a difference in blur degree of a plurality of images photographed by an imaging optical system; a correcting unit configured to correct the distance information using correction data in accordance with an image height in the imaging optical system; and an extraction unit configured to extract at least one frequency component from each of the plurality of images, wherein the calculation unit calculates the distance information from a difference in blur degree in the plurality of images in the at least one frequency component; and the correcting unit corrects the distance information using correction data in accordance with an image height in the at least one frequency component.

Embodiments relate to the field of image processing technologies, and in particular, to an image processing method and apparatus. In embodiments, when an image is being photographed, an exposure time that is required is first determined, and if the required exposure time is longer than a preset exposure time, the preset exposure time is used to photograph N second images, that is, and a final image is obtained by processing the N second images.

Provided are a three-dimensional (3D) image acquisition apparatus, and a method of generating a depth image in the 3D image acquisition apparatus. The method may include sequentially projecting a light transmission signal, which is generated from a light source, to a subject, modulating reflected light, which is reflected by the subject, using a light modulation signal, calculating a phase delay using a combination of a first plurality of images of two groups, from among a second plurality of images of all groups obtained by capturing the modulated reflected light, and generating a depth image based on the phase delay.