Machine learning based image processing techniques
AssigneeOutward, Inc.
InventorsClarence Chui, Manu Parmar
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Abstract
A machine learning based image processing architecture and associated applications are disclosed herein. In some embodiments, a machine learning framework is trained to learn low level image attributes such as object/scene types, geometries, placements, materials and textures, camera characteristics, lighting characteristics, contrast, noise statistics, etc. Thereafter, the machine learning framework may be employed to detect such attributes in other images and process the images at the attribute level.
Claims54
A method, comprising:
- using a machine learning framework to identify a set of filters for removing noise from a sparsely ray traced input image, wherein different filters are identified for different portions of the input image and wherein the set of filters predicts pixel values that would result from ray tracing with more samples of rays;
- and
- outputting an output image comprising a filtered version of the input image resulting from filtering the input image with the identified set of filters, wherein a quality of the output image is equivalent to ray tracing with more samples of rays than the sparsely ray traced input image.
A system, comprising:
- a processor configured to:
- use a machine learning framework to identify a set of filters for removing noise from a sparsely ray traced input image, wherein different filters are identified for different portions of the input image and wherein the set of filters predicts pixel values that would result from ray tracing with more samples of rays;
- and
- output an output image comprising a filtered version of the input image resulting from filtering the input image with the identified set of filters, wherein a quality of the output image is equivalent to ray tracing with more samples of rays than the sparsely ray traced input image;
- and
- a memory coupled to the processor and configured to provide the processor with instructions.
A computer program product embodied in a non-transitory computer readable storage medium and comprising computer instructions for:
- using a machine learning framework to identify a set of filters for removing noise from a sparsely ray traced input image, wherein different filters are identified for different portions of the input image and wherein the set of filters predicts pixel values that would result from ray tracing with more samples of rays;
- and
- outputting an output image comprising a filtered version of the input image resulting from filtering the input image with the identified set of filters, wherein a quality of the output image is equivalent to ray tracing with more samples of rays than the sparsely ray traced input image.
Dependencies are inferred from the claim text.