A method and system for correcting axial motion in optical coherence tomography (OCT) data is provided. The method includes collecting, by a processor disposed of in an OCT device, a volume scan of an eye; segmenting a first retinal layer within the volume scan; applying an algorithm for periodic pattern removal of OCT data in the first retinal layer by determining a model of a Fourier transform applicable to a segment of the first retinal layer; and removing transform frequencies associated with the OCT data using the model of the Fourier transform; determining a measure of an amount of axial motion in accordance with a difference of an amount of OCT data captured on a surface of the first retinal layer before and after application of the algorithm for periodic pattern removal; and correcting, the amount of axial motion in the OCT data of the first retinal layer.

Methods, systems, and apparatus, including computer programs stored on a computer-readable storage medium, for video stabilization. In some implementations, a computer system obtains frames of a video captured by a recording device using an optical image stabilization (OIS) system. The computing system receives (i) OIS position data indicating positions of the OIS system during capture of the frames, and (ii) device position data indicating positions of the recording device during capture of the frames. The computing system determines a first transformation for a particular frame based on the OIS position data for the particular frame and device position data for the particular frame. The computing system determines a second transformation for the particular frame based on the first transformation and positions of the recording device occurring after capture of the particular frame. The computing system generates a stabilized version of the particular frame using the second transformation.

In an example embodiment a method, apparatus and computer program product are provided. The method includes facilitating simultaneous capture of a first image by a first camera and a second image by a second camera associated with a device. One or more distortion parameters associated with a distortion in the second image may be determined based on a comparison of the second image with at least one template image associated with the second image. A distortion-free first image is generated based on the one or more distortion parameters associated with the second image by performing one of applying the one or more distortion parameters to the first image, and estimating one or more distortion parameters associated with the first image based on the one or more distortion parameters associated with the second image, and applying, the one or more distortion parameters associated with the first image to the first image.

A system for HDR image capture is configurable to perform a split long exposure operation by applying a first set of long exposure shutter operations to configure each sensor pixel of the image sensor array to enable photon detection and applying a second set of long exposure shutter operations to configure each sensor pixel to enable photon detection. A time period intervenes between the first and second sets of long exposure shutter. The system is configurable to perform a short exposure operation by applying a set of short exposure shutter operations to configure each sensor pixel to enable photon detection. The short exposure operation occurs during the time period that intervenes between the first and second sets of long exposure shutter operations. The system is also configurable to generate an image based on the split long exposure operation and the short exposure operation.

In an example embodiment a method, apparatus and computer program product are provided. The method includes determining presence of at least one moving object in a scene based on two or more burst images corresponding to the scene captured by a first camera. One or more portions of the scene associated with the at least one moving object are identified, and, information related to the one or more portions is provided to a second camera. An image of the scene captured by the second camera second camera is received, where a pixel level shutter disposed in front of an image sensor of the second camera is programmed to periodically open and close, throughout a duration of said image capture, for pixels of the image sensor corresponding to the one or more portions of the scene. A deblurred image corresponding to the scene is generated based on the image.

A method for mitigating motion blur in a visual-inertial tracking system is described. In one aspect, the method includes accessing a first image generated by an optical sensor of the visual tracking system, accessing a second image generated by the optical sensor of the visual tracking system, the second image following the first image, determining a first motion blur level of the first image, determining a second motion blur level of the second image, identifying a scale change between the first image and the second image, determining a first optimal scale level for the first image based on the first motion blur level and the scale change, and determining a second optimal scale level for the second image based on the second motion blur level and the scale change.

In an example embodiment, method, apparatus and computer program product are provided. The method includes facilitating receipt of a deconvolved image including a plurality of component images. A guide image is selected from the component images and a cross-filtering is performed of component images other than the guide image to generate filtered component images. The cross-filtering is performed of a component image by iteratively performing, selecting a pixel and a set of neighboring pixels around the pixel in the guide image, computing a set of weights corresponding to the set of neighboring pixels based at least on spatial differences between the pixel and the set of neighboring pixels, and cross-filtering a corresponding pixel of the pixel in the component image based on the set of weights to generate a filtered corresponding pixel in the component image. The filtered component images form a filtered deconvolved image with reduced chromatic aberration.

Embodiments of the present application provide image processing methods and apparatus. A image processing method disclosed herein comprises: acquiring, from an image, two regions which have a textural similarity higher than a first value and have different depths; performing frequency-domain conversion on each of the regions, to obtain a frequency-domain signal of each region; and optimizing the image at least according to the frequency-domain signal of each region, the depth of each region and a focusing distance of the image.

Techniques for using motion data to generate a high resolution output color image from multiple images having sparse color information are disclosed. A camera generates multiple images. The camera's sensor is configured to have a sparse Bayer pattern. While the camera is generating the images, IMU data for each image is acquired. The IMU data indicates a corresponding pose the camera was in while the camera generated each image. The images and the IMU data are fed as input into a motion model. The motion model performs temporal filtering on the images and uses the IMU data to generate a red-only image, a green-only image, and a blue-only image. A high resolution output color image is generated by combining the red-only image, the green-only image, and the blue-only image.

A method of generating a de-interlacing filter comprises: analysing a pixel array comprising an interlacing pattern of pixels. The interlacing pattern of pixels comprises first and second pluralities of pixels configured to be read during a first measurement subframe and a second measurement subframe, respectively. An n-state representation of the interlacing pattern of pixels is generated and distinguishes between the first plurality of pixels and the second plurality of pixels. The n-state representation of the interlacing pattern is translated to a spatial frequency domain, thereby generating a spatial frequency domain representation of the n-state representation of the interlacing pattern. A DC signal component is then removed from the spatial frequency domain representation of the n-state representation of the interlacing pattern, thereby generating a DC-less spatial frequency domain representation. A kernel filter is then selected and configured to blur before convolving the DC-less spatial frequency representation with the selected kernel filter.