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公开(公告)号：US20220148135A1
公开(公告)日：2022-05-12
申请号：US17093852
申请日：2020-11-10
Applicant: Adobe Inc.
Inventor： Mustafa Isik , Michael Yanis Gharbi , Matthew David Fisher , Krishna Bhargava Mullia Lakshminarayana , Jonathan Eisenmann , Federico Perazzi
Abstract: A plurality of pixel-based sampling points are identified within an image, wherein sampling points of a pixel are distributed within the pixel. For individual sampling points of individual pixels, a corresponding radiance vector is estimated. A radiance vector includes one or more radiance values characterizing light received at a sampling point. A first machine learning module generates, for each pixel, a corresponding intermediate radiance feature vector, based on the radiance vectors associated with the sampling points within that pixel. A second machine learning module generates, for each pixel, a corresponding final radiance feature vector, based on an intermediate radiance feature vector for that pixel, and one or more other intermediate radiance feature vectors for one or more other pixels neighboring that pixel. One or more kernels are generated, based on the final radiance feature vectors, and applied to corresponding pixels of the image, to generate a lower noise image.
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公开(公告)号：US11983854B2
公开(公告)日：2024-05-14
申请号：US17093852
申请日：2020-11-10
Applicant: Adobe Inc.
Inventor： Mustafa Isik , Michael Yanis Gharbi , Matthew David Fisher , Krishna Bhargava Mullia Lakshminarayana , Jonathan Eisenmann , Federico Perazzi
Abstract: A plurality of pixel-based sampling points are identified within an image, wherein sampling points of a pixel are distributed within the pixel. For individual sampling points of individual pixels, a corresponding radiance vector is estimated. A radiance vector includes one or more radiance values characterizing light received at a sampling point. A first machine learning module generates, for each pixel, a corresponding intermediate radiance feature vector, based on the radiance vectors associated with the sampling points within that pixel. A second machine learning module generates, for each pixel, a corresponding final radiance feature vector, based on an intermediate radiance feature vector for that pixel, and one or more other intermediate radiance feature vectors for one or more other pixels neighboring that pixel. One or more kernels are generated, based on the final radiance feature vectors, and applied to corresponding pixels of the image, to generate a lower noise image.
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