A head-mounted display device including one or more position sensors and a processor. The processor may receive a rendered image of a current frame. The processor may receive position data from the one or more position sensors and determine an updated device pose based on the position data. The processor may apply a first spatial correction to color information in pixels of the rendered image at least in part by reprojecting the rendered image based on the updated device pose. The head-mounted display device may further include a display configured to apply a second spatial correction to the color information in the pixels of the rendered image at least in part by applying wobulation to the reprojected rendered image to thereby generate a sequence of wobulated pixel subframes for the current frame. The display may display the current frame by displaying the sequence of wobulated pixel subframes.

A lens and color filter assembly contains lens units, and each lens unit is assigned to a single-color color filter unit. The lens and color filter assembly may be combined with pixel units such that a plurality of monochromatic, low-resolution images can be obtained, and the monochromatic images refer to shifted versions of the same image object. By a super-resolution technique comprising shift-compensation a mosaicked image is obtained which is then demosaiced. In the resultant image only few artifacts appear. Simple color filter arrays allow a simplified fabrication process and provide less chromatic aberrations at less computational effort.

Systems and methods in accordance with embodiments of the invention enable feature based high resolution motion estimation from low resolution images captured using an array camera. One embodiment includes performing feature detection with respect to a sequence of low resolution images to identify initial locations for a plurality of detected features in the sequence of low resolution images, where the at least one sequence of low resolution images is part of a set of sequences of low resolution images captured from different perspectives. The method also includes synthesizing high resolution image portions, where the synthesized high resolution image portions contain the identified plurality of detected features from the sequence of low resolution images. The method further including performing feature detection within the high resolution image portions to identify high precision locations for the detected features, and estimating camera motion using the high precision locations for said plurality of detected features.

Improved HMDs are described. The HMD is configured to provide higher resolution image content within a central area of a display of the HMD and to provide lower resolution image content within peripheral areas of the display. The HMD includes a higher resolution camera, a first lower resolution camera, and a second lower resolution camera. The FOV of the first lower resolution camera minimally overlaps the FOV of the higher resolution camera, and the FOV of the second lower resolution camera minimally overlaps the FOV of the higher resolution camera. The minimal overlap is less than 10 degrees. By structuring the HMD in this manner, the HMD can generate merged images that mimic a user's foveal vision acuity.

A system includes at least one imaging sensor configured to capture images of a target. The system also includes at least one controller configured to generate super-resolution images of the target using the captured images and identify multiple edges of the target using the super-resolution images. The at least one controller is also configured to identify an aimpoint on the target based on the identified edges of the target. In addition, the at least one controller is configured to update the aimpoint on the target as the target moves over time. The system may further include a high-energy laser (HEL) configured to generate an HEL beam that is directed towards the target, and the at least one controller may be configured to adjust one or more optical devices to direct the HEL beam at the identified aimpoint on the target.

A computer-implemented method for a classification and training a neural network includes receiving input at the neural network, wherein the input includes a plurality of resolution inputs of varying resolutions, outputting a plurality of feature tensors for each corresponding resolution of the plurality of resolution inputs, fusing the plurality of feature tensors utilizing upsampling or down sampling for the vary resolutions, utilizing an equilibrium solver to identify one or more prediction vectors from the plurality of feature tensors, and outputting a loss in response to the one or more prediction vectors.

A head-mounted display device including one or more position sensors and a processor. The processor may receive a rendered image of a current frame. The processor may receive position data from the one or more position sensors and determine an updated device pose based on the position data. The processor may apply a first spatial correction to color information in pixels of the rendered image at least in part by reprojecting the rendered image based on the updated device pose. The head-mounted display device may further include a display configured to apply a second spatial correction to the color information in the pixels of the rendered image at least in part by applying wobulation to the reprojected rendered image to thereby generate a sequence of wobulated pixel subframes for the current frame. The display may display the current frame by displaying the sequence of wobulated pixel subframes.

An electronic device that outputs at least one calibration point through a display, obtains gaze information corresponding to the at least one calibration point by using a gaze tracking sensor in response to an output of guide information instructing a user wearing the electronic device to gaze at the at least one calibration point, obtains a gaze accuracy corresponding to the at least one calibration point based on the obtained gaze information, determines a gaze zone-specific resolution based on the gaze accuracy corresponding to the at least one calibration point, and outputs an image through the display based on the determined gaze zone-specific resolution.

The present disclosure describes systems and techniques directed to optical image stabilization movement to create a super-resolution image of a scene. The systems and techniques include a user device (102) introducing (502), through an optical image stabilization system (114), movement to one or more components of a camera system (112) of the user device (102). The user device (102) then captures (504) respective and multiple frames (306) of an image of a scene, where the respective and multiple frames (306) of the image of the scene have respective, sub-pixel offsets of the image of the scene across the multiple frames (306) as a result of the introduced movement to the one or more components of the camera system (112). The user device (102) performs (506), based on the respective, sub-pixel offsets of the image of the scene across the respective, multiple frames (306), super-resolution computations and creates (508) the super-resolution image of the scene based on the super-resolution computations.

The technology disclosed relates to structured illumination microscopy (SIM). In particular, the technology disclosed relates to capturing and processing, in real time, numerous image tiles across a large image plane, dividing them into subtiles, efficiently processing the subtiles, and producing enhanced resolution images from the subtiles. The enhanced resolution images can be combined into enhanced images and can be used in subsequent analysis steps. The technology disclosed includes logic to reduce computing resources required to produce an enhanced resolution image from structured illumination of a target. A method is described for producing an enhanced resolution image from images of a target captured under structured illumination. This method applies one or more transformations to non-redundant data and then recovers redundant data from the non-redundant data after the transformations.