De-noising images using machine learning

A non-transitory computer-readable medium storing a computer program that when executed by a processor of a computer cause the processor to implement a computer-implemented method comprising:

• receiving a plurality of noisy subimages, wherein a subimage is at least a portion of an image, and wherein each of the plurality of noisy subimages is rendered using a respective set of rays in a corresponding computer-generated scene;
• receiving a plurality of refined subimages, each refined subimage corresponding to a noisy subimage of the plurality of noisy subimages and generated using a corresponding computer-generated scene, wherein a refined subimage has reduced noise relative to a corresponding noisy subimage;
• extracting values of a first set of features from pixels in each of the plurality of noisy subimages;
• determining values of a second set of features from information obtained from the corresponding computer-generated scenes from which the plurality of noisy subimages were generated;
• training a neural network based on the values of the features and the plurality of refined subimages, wherein the neural network outputs pixel values of an output subimage or outputs gradients of pixels of the output subimage, and wherein the training includes:
• generating, using the neural network, an output subimage for a noisy subimage of the plurality of noisy subimages;
• comparing the output subimage to a refined subimage of the plurality of refined subimages that corresponds to the noisy subimage;
• and
• modifying one or more parameters of the neural network based on the comparison;
• receiving a new two-dimensional image rendered from a computer-generated scene;
• and
• generating, using the neural network after training, a new output image corresponding to the new two-dimensional image, wherein the neural network is a first neural network, wherein the first set of features includes an illumination of pixels of the noisy subimage, wherein the first neural network is trained based on a first light path, wherein a second neural network is trained based on a second light path, and wherein output of the first neural network and the second neural network are combined to generate the new output image.

A method for reducing noise in a two-dimensional image obtained from a computer-generated scene, the method comprising:

• receiving a plurality of noisy subimages, wherein a subimage is at least a portion of an image, and wherein each of the plurality of noisy subimages is rendered using a respective set of rays in a corresponding computer-generated scene;
• receiving a plurality of refined subimages, each refined subimage corresponding to a noisy subimage of the plurality of noisy subimages and generated using a corresponding computer-generated scene, wherein a refined subimage has reduced noise relative to a corresponding noisy subimage;
• extracting values of a first set of features from pixels in each of the plurality of noisy subimages;
• determining values of a second set of features from information obtained from the corresponding computer-generated scenes from which the plurality of noisy subimages were generated;
• training a neural network based on the values of the features and the plurality of refined subimages, wherein the neural network outputs pixel values of an output subimage or outputs gradients of pixels of the output subimage, and wherein the training includes:
• generating, using the neural network, an output subimage for a noisy subimage of the plurality of noisy subimages;
• comparing the output subimage to a refined subimage of the plurality of refined subimages that corresponds to the noisy subimage;
• and
• modifying one or more parameters of the neural network based on the comparison;
• receiving a new two-dimensional image rendered from a computer-generated scene;
• and
• generating, using the neural network after training, a new output image corresponding to the new two-dimensional image, wherein one of the first set of features is a variance of values of different rays for a pixel.

A computing system comprising:

• one or more processors configured to:
• receive a plurality of noisy subimages, wherein a subimage is at least a portion of an image, and wherein each of the plurality of noisy subimages is rendered using a respective set of rays in a corresponding computer-generated scene;
• receive a plurality of refined subimages, each refined subimage corresponding to a noisy subimage of the plurality of noisy subimages and generated using a corresponding computer-generated scene, wherein a refined subimage has reduced noise relative to a corresponding noisy subimage;
• extract values of a first set of features from pixels in each of the plurality of noisy subimages;
• determine values of a second set of features from information obtained from the corresponding computer-generated scenes from which the plurality of noisy subimages were generated;
• train a neural network based on the values of the features and the plurality of refined subimages, wherein the neural network outputs pixel values of an output subimage or outputs gradients of pixels of the output subimage, and wherein the training includes:
• generating, using the neural network, an output subimage for a noisy subimage of the plurality of noisy subimages;
• comparing the output subimage to a refined subimage of the plurality of refined subimages that corresponds to the noisy subimage;
• and
• modifying one or more parameters of the neural network based on the comparison;
• receive a new two-dimensional image rendered from a computer-generated scene;
• and
• generate, using the neural network after training, a new output image corresponding to the new two-dimensional image, wherein one of the first set of features is a variance of values of different rays for a pixel.