Research in Motion Corporation

Blackberry Corporation

Samsung Electronics Co., Ltd.

Other

Adobe, Inc.

York University

Research in Motion (tx Office)

Michael S Brown

Herbert Anthony Little

Michael Kenneth Brown

Neil Patrick Adams

Michael Grant Kirkup

David Francis Tapuska

Jason Tyler Griffin

Mihal Lazaridis

David Paul Yach

Scott William Totzke

Terrill Mark Dent

Harry Richmond Major

James Andrew Godfrey

Ian M Robertson

Craig Allan Dunk

John A Holmes

Jonathan F Hammell

Christopher Lyle Bender

Russell Norman Owen

Herb A Little

David Victor MacFarlane

Gerhard Dietrich Klassen

Steven Henry Fyke

Gary P Mousseau

Anthony Fabian Scian

Michael Edwin McCallum

Robert James Lowles

Salim Omar

Dinah Lea Marie Davis

Timothy Richard Tyhurst

Carl Lloyd Cherry

Timothy Lee Segato

Tomasz K Rybak

Kalu Onuka Kalu

George V Babu

Abdelrahman Abdelhamed

Jérôme Lang

Mahmoud Nasser Mohammed Afifi

Jerome Lang

Abhijith Punnappurath

Salim H Omar

Andrew Douglas Bocking

Alexander Sherkin

Luis Pablo Estable

Dan Zacharias Gärdenfors

Michael Hin Kai Hung

Michael Hung

Dinah Davis

Theban Ganeshalingam

Albert Hecht-Enns

Mahmoud Afifi

Kevin J Bailey

J&eacute;r&ocirc;me Lang

Sandra Marcela Küpfer

Michael E Mccallum

Michael Mccallum

Scott David Cohen

Sherryl Lee Lorraine Scott

Brian Lynn Price

Raymond Paul Vander Veen

Robert John Lambert

Kihwan Kim

David Clark

Alfred John Menezes

Orlin Vesselinov Stoev

Mathias Lewin

Jihwan Choe

Graham Russell

Marcus Anthony Brubaker

Leif Fredrik Ademar

Konstantinos Derpanis

Jonghwa Yim

Brandon James Dehart

Aleksai Levinshtein

Ryan Gregory Wood

Marcel Florin Leica

Gerhard Dietrick Klassen

Hakki Can Karaimer

Ian D Kayser

Steven L Kayser

Björn Ommer

Ali Maleky

Abdullah Abuolaim

Luxi Zhao

Spencer William Bloom

Sandra Marcela Kupfer

Mike Kirkup

David R Clark

George Babu

David P Tapuska

Dinah Lea Davis

Peter Jan Herniczek

Kalu O Kalu

Dinah L Davis

Scott W Totzke

Michael K Brown

Terrill Dent

A noise model is iteratively trained to simulate introduction of noise by a capture device, by use of a denoiser and a training data set of pairs of noisy signals. First and second noisy signals of each pair are independently sampled by the capture device from source information corresponding to the pair. Each iteration of training obtains first and second denoised signals from respective noisy signals, then optimizes at least one loss function which sums first and second terms to train both the noise model and the denoiser, where the first term is based on the first denoised signal and the second noisy signal, and the second term is based on the second denoised signal and the first noisy signal. By using noisy samples, the complexities of obtaining 'clean' signals are avoided. By using “cross-sample” loss functions, convergence on undesired training results is avoided without complex regularization.

System and method for training of noise model using noisy signal pairs

An apparatus for white balance editing, includes a memory storing instructions, and at least one processor configured to execute the instructions to obtain an input image having an original white balance that is corrected by image signal processing, and obtain, using a first neural network, an intermediate representation of the obtained input image, the intermediate representation having the original white balance that is not corrected by the image signal processing. The at least one processor is further configured to execute the instructions to obtain, using a second neural network, a first output image having a first white balance different than the original white balance, based on the obtained intermediate representation.

Apparatus and method for white balance editing

A method is provided. The method includes receiving an input image, extracting at least one feature from the input image, determining at least one local tone curve for a portion of the input image based on the extracted at least one feature, the portion of the input image being less than an overall area of the input image, and generating a toned image based on the at least one local tone curve.

System and method for learning tone curves for local image enhancement

A method of generating a low-light image is provided. The method includes receiving a raw image, removing an amount of first illumination from the raw image, applying a low exposure condition to the raw image having the amount of first illumination removed, and applying an amount of low-light illumination to the raw image having the applied low exposure condition.

System and method for synthesizing low-light images

A method for generating an output image may include obtaining an input image having a first exposure value, generating a plurality of levels that are each respectively associated with a respective representation of the input image, based on the input image, generating the output image having a second exposure value, based on a deep neural network (DNN) including a set of sub-networks and the plurality of levels, and providing the output image.

Network for correcting overexposed and underexposed images

A method of generating a display object is provided, the method comprising operating a first processor comprised within a first device to: execute an application; generate an display object associated with the application; output the active display object on the first device; transmit the display object to a second device for output by the second device; and receive user input to the application via the display object output on the second device.

Updating an application at a second device based on received user input at a first device

A method for setting parameter settings of an image signal processor (ISP) of a camera of a mobile device based on a user selection may include controlling a display of the mobile device to display a plurality of images, each of which is associated with corresponding parameter settings. The method may include receiving, via a user interface (UI) of the mobile device, the user selection in relation to an image of the plurality of images. The method may include determining selected parameter settings based on the user selection in relation to the image. The method may include setting the parameter settings of the ISP, based on the selected parameter settings, to permit the ISP to process a raw image from an image sensor of the mobile device using the selected parameter settings.

System and method for image color management

A method for generating an image that includes at least one of a vignette effect or a grain effect corresponding to an input image may include obtaining the input image including at least one of the vignette effect or the grain effect; identifying at least one of a vignette parameter or a grain parameter of the input image; obtaining at least one of a vignette filter based on the vignette parameter or a grain layer based on the grain parameter; and generating the image that includes at least one of the vignette effect or the grain effect by applying at least one of the vignette filter or the grain layer to the image.

System and method for obtaining and applying a vignette filter and grain layer

Systems and methods for sensor-independent illuminant determination are provided. In one embodiment of the method, the method includes receiving one or more training images in raw-RGB format; generating an input histogram from each of the inputted raw images; generating a learned mapping matrix that map raw images to a learned mapping space by passing the one or more input histograms to a trained first machine learning model; generating one or more mapped images by applying the learned mapping matrix to the one or more training images; generating a mapped histogram from each of the mapped images; and determining the result illuminant by passing the one or more mapped histograms as input into a second machine learning model. A final illuminant for an input image can be determined by applying the result illuminant to the input color space of the input image.

Systems and methods for sensor-independent illuminant determination

An apparatus for customizing camera parameters is provided. The apparatus includes: an image signal processor; and a processor configured to execute instructions to: generate a first edited image based on a first original image, the first original image including first original image data and first metadata indicating a first pattern of processed RGB colors; identify a first mapping function by analyzing the first pattern with respect to a fixed pattern of unprocessed-RGB color space samples; generate first unprocessed image data based on the first mapping function and the first original image; identify image signal processor adjustment parameters based on the first unprocessed image data and the first edited image; and image signal processor parameters of the image signal processor based on the image signal processor adjustment parameters.

Method and system for customizing camera parameters

A system and method for reflection removal of an image from a dual-pixel sensor. The image including a left view and a right view. The method including: determining a first gradient of the left view and a second gradient of the right view; determining disparity between the first gradient and the second gradient using a sum of squared differences (SSD); determining a confidence value at each pixel using the SSD; determining a weighted gradient map using the confidence values; minimizing a cost function to estimate the background layer, the cost function including the weighted gradient map, wherein the image includes the background layer added to a reflection layer; and outputting at least one of the background layer and the reflection layer.

System and method for reflection removal using dual-pixel sensor

Computer-based systems, methods and computer program products for colorimetric mapping are provided. In one embodiment of the method, the method includes obtaining a digital reference image, including: illuminating a known reference source with an arbitrary illumination; and determining an observed illumination by receiving light signals from the reference source at an image sensor; determining a color-balance matrix by minimizing an error between colorimetric values of the digital reference image and color corrected pixel values; applying the color-balance matrix to the digital reference image to produce a color-corrected image; determining a color-space transformation matrix that transforms a color space of the image sensor to a perceptual color space; generating a transformed image by applying the color-space transformation matrix to the color corrected image; and outputting the transformed image.

Systems, methods and computer programs for colorimetric mapping

Embodiments of the systems, devices and methods described herein generally facilitate performing Bluetooth pairing between a first device and a second device. In accordance with one example embodiment, a first device generates a barcode that encodes Bluetooth pairing data for transmission to the second device, wherein the pairing data comprises a Bluetooth address associated with the first device, and wherein the pairing data further comprises a personal identification number usable to complete the Bluetooth pairing, the barcode is transmitted to the second device, and Bluetooth pairing is performed with the second device.

Methods and devices for facilitating bluetooth pairing using a camera as a barcode scanner

Techniques are described for white balancing an input image by determining a color transformation for the input image based on color transformations that have been computed for training images whose color characteristics are similar to those of the input image. Techniques are also described for generating a training dataset comprising color information for a plurality of training images and color transformation information for the plurality of training images. The color information in the training dataset is searched to identify a subset of training images that are most similar in color to the input image. The color transformation for the input image is then computed by combining color transformation information for the identified training images. The contribution of the color transformation information for any given training image to the combination can be weighted based on the degree of color similarity between the input image and the training image.

Image white balancing

Growing community of inventors

Waterloo, Canada

Michael S Brown