Nvidia Corporation

King Abdullah University of Science and Technology

Algolux Inc.

Massachusetts Institute of Technology

The University of North Carolina at Chapel Hill

Princeton University

The University of British Columbia

Felix Heide

Jan Kautz

Wolfgang Heidrich

Kari Pulli

Mushfiqur Rouf

Douglas Robert Lanman

David Patrick Luebke

Gordon Wetzstein

Orazio Gallo

Dawid Stanislaw Pajak

Dikpal Reddy

Nagilla Dikpal Reddy

Matthias Hullin

Ramesh Raskar

Qiang Fu

James Gregson

Praneeth Kumar Chakravarthula

Seung-Hwan Baek

Ilya Chugunov

The microlens amplitude masks for flying pixel removal in time-of-flight imaging includes systems, devices, methods, and instructions for image depth determination, including receiving an image, adding noise to the image, determining a set of correlation images, each correlation image having a varying phase offset, for each pixel of the image, generating a masked pixel by applying a mask array, and for each masked pixel, determining the depth of the masked pixel to generate a depth map for the image on a per pixel basis.

Microlens amplitude masks for flying pixel removal in time-of-flight imaging

System and method for joint refinement and perception of images are provided. A learning machine employs an image acquisition device for acquiring a set of training raw images. A processor determines a representation of a raw image, initializes a set of image representation parameters, defines a set of analysis parameters of an image analysis network configured to process the image's representation, and jointly trains the set of representation parameters and the set of analysis parameters to optimize a combined objective function. A module for transforming pixel-values of the raw image to produce a transformed image comprising pixels of variance-stabilized values, a module for successively performing processes of soft camera projection and image projection, and a module for inverse transforming the transformed pixels are disclosed. The image projection performs multi-level spatial convolution, pooling, subsampling, and interpolation.

System and method for joint image refinement and perception

A method for digital holography includes modeling a hologram using a forward propagation model that models propagation of a light field from a hologram plane to an image plane. The method further includes computing the hologram as a solution to an optimization problem that is based on the model. The method further includes configuring at least one spatial light modulator using the hologram. The method further includes illuminating the spatial light modulator using a light source to create a target image.

Methods, systems, and computer readable media for improved digital holography and display incorporating same

Systems and methods for imaging object velocity are provided. In an embodiment, at least one Time-of-Flight camera is used to capture a signal representative of an object in motion over an exposure time. Illumination and modulation frequency of the captured motion are coded within the exposure time. A change of illumination frequency is mapped to measured pixel intensities of the captured motion within the exposure time, and information about a Doppler shift in the illumination frequency is extracted to obtain a measurement of instantaneous per pixel velocity of the object in motion. The radial velocity information of the object in motion can be simultaneously captured for each pixel captured within the exposure time. In one or more aspects, the illumination frequency can be coded orthogonal to the modulation frequency of the captured motion. The change of illumination frequency can correspond to radial object velocity.

Doppler time-of-flight imaging

A learning machine employs an image acquisition device for acquiring a set of training raw images. A processor determines a representation of a raw image, initializes a set of image representation parameters, defines a set of analysis parameters of an image analysis network configured to process the image's representation, and jointly trains the set of representation parameters and the set of analysis parameters to optimize a combined objective function. Processor executable instructions are organized into a module for transforming pixel-values of the raw image to produce a transformed image comprising pixels of variance-stabilized values, a module for successively performing processes of soft camera projection and image projection, and a module for inverse transforming the transformed pixels. The image projection process performs multi-level spatial convolution, pooling, subsampling, and interpolation.

Method and apparatus for joint image processing and perception

System and method of displaying images in temporal superresolution by multiplicative superposition of cascaded display layers integrated in a display device. Using an original video with a target temporal resolution as a priori, a factorization process is performed to derive respective image data for presentation on each display layer. The multiple layers are refreshed in staggered intervals to synthesize a video with an effective refresh rate exceeding that of each individual display layer, e.g., by a factor equal to the number of layers. Further optically averaging neighboring pixels can minimize artifacts.

Superresolution display using cascaded panels

A computer implemented method of determining a latent image from an observed image is disclosed. The method comprises implementing a plurality of image processing operations within a single optimization framework, wherein the single optimization framework comprises solving a linear minimization expression. The method further comprises mapping the linear minimization expression onto at least one non-linear solver. Further, the method comprises using the non-linear solver, iteratively solving the linear minimization expression in order to extract the latent image from the observed image, wherein the linear minimization expression comprises: a data term, and a regularization term, and wherein the regularization term comprises a plurality of non-linear image priors.

Unified optimization method for end-to-end camera image processing for translating a sensor captured image to a display image

System and method of displaying images in spatial/temporal superresolution by multiplicative superposition of cascaded display layers integrated in a display device. Using an original image with a target spatial/temporal resolution as a priori, a factorization process is performed to derive respective image data for presentation on each display layer. The cascaded display layers may be progressive and laterally shifted with each other, resulting in an effective spatial resolution exceeding the native display resolutions of the display layers. Factorized images may be refreshed on respective display layers in synchronization or out of synchronization.

In exemplary implementations of this invention, light from a backlight is transmitted through two stacked LCDs and then through a diffuser. The front side of the diffuser displays a time-varying sequence of 2D images. Processors execute an optimization algorithm to compute optimal pixel states in the first and second LCDs, respectively, such that for each respective image in the sequence, the optimal pixel states minimize, subject to one or more constraints, a difference between a target image and the respective image. The processors output signals to control actual pixel states in the LCDs, based on the computed optimal pixel states. The 2D images displayed by the diffuser have a higher spatial resolution than the native spatial resolution of the LCDs. Alternatively, the diffuser may be switched off, and the device may display either (a) 2D images with a higher dynamic range than the LCDs, or (b) an automultiscopic display.

Methods and apparatus for visual display

There is provided a computer-implemented method for solving inverse imaging problems to compensate for distortions in an image. The method comprises: minimizing a cost objective function containing a data fitting term and one or more image prior terms to each of the plurality of channels, the one or more image prior terms comprising cross-channel information for a plurality of channels derived from the image.

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Vancouver, Canada

Felix Heide