Nima Khademi Kalantari

20160321523, Nov 3, 2016

May 2, 2016

15/144613

- Oakland CA, US
Nima Khademi Kalantari - La Jolla CA, US
Steve Bako - Santa Barbara CA, US

G06K 9/66
G06K 9/62
G06T 5/20
G06K 9/46
G06T 5/00

A method of producing noise-free images is disclosed. The method includes using machine learning incorporating a filter to output filter parameters using the training images. The machine learning may include training a neural network. The filter parameters are applied to Monte Carlo rendered training images that have noise to generate noise-free images. The training may include determining, computing and extracting features of the training images; computing filter parameters; applying an error metric; and applying backpropgation. The neural network may be a multilayer perceptron. The machine learning model is applied to new noisy Monte Carlo rendered images to create noise-free images. This may include applying the filter to the noisy Monte Carlo rendered images using the filter parameters to create the noise-free images.

20150235399, Aug 20, 2015

Feb 20, 2014

14/185507

Daniel R. Goldman - Seattle WA, US
Aliakbar Darabi - Albuquerque NM, US
Nima Khademi Kalantari - Santa Barbara CA, US

G06T 11/60

Variable patch shape synthesis techniques are described. In one or more implementations, a plurality of patches are computed from one or more images, at least one of the plurality of patches having a different shape than another one of the plurality of patches. The shapes define an area to be considered for use in a patch synthesis technique. The patch synthesis technique is performed to edit an image using the computed plurality of patches having the different shapes.