A method, system, and apparatus for license plate relighting comprises collecting an image of a license plate, performing license plate recognition on the image of the license plate; calculating a confidence metric for the license plate recognition; and performing a shadow detection and relighting method if the confidence metric is below a predetermined threshold, comprising identifying a shaded region of said license plate, determining if the shaded region is actually shaded, and relighting the actually shaded region.

An example apparatus for compressing dynamic range includes an image receiver to receive an input image with a high dynamic range. The apparatus further includes a darkness gamma transfer calculator to calculate gain values for each output pixel via a darkness gamma transfer function. The apparatus also further includes a gain applicator to apply the gain values to color channel values of the input image to generate a compressed image.

Various methods and systems are provided for image processing for multiple cameras. In one embodiment, a method comprises acquiring image frames with a plurality of image frame sources configured with different acquisition settings, processing the image frames based on the different acquisition settings to generate at least one final image frame, and outputting the at least one final image frame. In this way, information from different image frame sources such as cameras may be leveraged to achieve increased frame rates with improved image quality and a desired motion appearance.

Embodiments discussed herein facilitate segmentation of vascular plaque, training a deep learning model to segment vascular plaque, and/or informing clinical decision-making based on segmented vascular plaque. One example embodiment accessing vascular imaging data for a patient, wherein the vascular imaging data comprises a volume of interest; pre-process the vascular imaging data to generate pre-processed vascular imaging data; provide the pre-processed vascular imaging data to a deep learning model trained to segment a lumen and a vascular plaque; and obtain segmented vascular imaging data from the deep learning model, wherein the segmented vascular imaging data comprises a segmented lumen and a segmented vascular plaque in the volume of interest.

Methods and systems for depth-based foveated rendering in the display system are disclosed. The display system may be an augmented reality display system configured to provide virtual content on a plurality of depth planes using different wavefront divergence. Some embodiments include determining a fixation point of a user's eyes. Location information associated with a first virtual object to be presented to the user via a display device is obtained. A resolution-modifying parameter of the first virtual object is obtained. A particular resolution at which to render the first virtual object is identified based on the location information and the resolution-modifying parameter of the first virtual object. The particular resolution is based on a resolution distribution specifying resolutions for corresponding distances from the fixation point. The first virtual object rendered at the identified resolution is presented to the user via the display system.

A method and apparatus analyze a difference of at least two gradings of an image on the basis of: obtaining a first graded picture (LDR) with a first luminance dynamic range; obtaining data encoding a grading of a second graded picture (HDR) with a second luminance dynamic range, different from the first luminance dynamic range; and determining a grading difference data structure (DATGRAD) on the basis of at least the data encoding the grading of the second graded picture (HDR), which allows more intelligently adaptive encoding of the imaged scenes, and consequently also better use of those pictures, such as higher quality rendering under various rendering scenarios.

The present disclosure is directed toward systems and methods that efficiently and effectively generate an enhanced document image of a displayed document in an image frame captured from a live image feed. For example, systems and methods described herein apply a document enhancement process to a displayed document in an image frame that result in an enhanced document image that is cropped, rectified, un-shadowed, and with dark text against a mostly white background. Additionally, systems and method described herein determine whether a stored digital content item includes a displayed document. In response to determining that a stored digital content item does include a displayed document, systems and methods described herein generate an enhanced document image of a displayed document included in the stored digital content item.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

An apparatus for recognizing an object in an image includes a preprocessing module configured to receive an image including an object and to output a preprocessed image by performing image enhancement processing on the received image to improve a recognition rate of the object included in the received image; and an object recognition module configured to recognize the object included in the image by inputting the preprocessed image to an input layer of an artificial neural network for object recognition.

An apparatus for recognizing an object in an image includes a preprocessing module configured to receive an image including an object and to output a preprocessed image by performing image enhancement processing on the received image to improve a recognition rate of the object included in the received image; and an object recognition module configured to recognize the object included in the image by inputting the preprocessed image to an input layer of an artificial neural network for object recognition.