Denoising Monte Carlo renderings using machine learning with importance sampling

A method of denoising images rendered by Monte Carlo (MC) path tracing, the method comprising:

• receiving a set of input images rendered by MC path tracing and a set of reference images, each reference image corresponding to a respective input image;
• configuring a neural network comprising:
• an input layer configured to receive the set of input images;
• a plurality of hidden layers, each hidden layer having a respective number of nodes, each node associated with a respective parameter, a first layer of the plurality of hidden layers coupled to the input layer;
• and
• an output layer coupled to a last layer of the plurality of hidden layers, the output layer configured to output a respective denoised image corresponding to a respective input image;
• training the neural network using the set of input images and the set of reference images, the training comprising:
• performing one or more initial iterations of the training by randomly sampling the set of input images using a uniform probability to obtain a set of intermediate denoised images, each intermediate denoised image corresponding to a respective input image;
• for each respective input image, evaluating an error gradient by comparing a corresponding intermediate denoised image to the respective input image;
• and
• performing one or more additional iterations of the training by sampling the set of input images according to a probability function based on the error gradient of each input image of the set of input images to obtain a set of optimized parameters associated with the nodes of the plurality of hidden layers;
• receiving a new input image rendered by MC path tracing;
• and
• generating a new denoised image corresponding to the new input image by passing the new input image through the neural network using the set of optimized parameters.

A method of denoising images rendered by Monte Carlo (MC) path tracing, the method comprising:

• receiving a set of input images rendered by MC path tracing and a set of reference images, each reference image corresponding to a respective input image;
• configuring a neural network comprising:
• an input layer configured to receive the set of input images;
• a plurality of hidden layers, each hidden layer having a respective number of nodes, each node associated with a respective parameter, a first layer of the plurality of hidden layers coupled to the input layer;
• and
• an output layer coupled to a last layer of the plurality of hidden layers, the output layer configured to output a respective denoised image corresponding to a respective input image;
• training the neural network using the set of input images and the set of reference images, the training comprising:
• assigning a relevance score to each respective input image of the set of input images, the relevance score indicating a degree of relevance to one or more areas of interests;
• and
• performing the training by sampling the set of input images according to a probability function that is proportional to the relevance score of each respective input image to obtain a set of optimized parameters associated with the nodes of the plurality of hidden layers;
• receiving a new input image rendered by MC path tracing;
• and
• generating a denoised image corresponding to the new input image by passing the new input image through the neural network using the set of optimized parameters.