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公开(公告)号:US10979640B2
公开(公告)日:2021-04-13
申请号:US16789195
申请日:2020-02-12
Applicant: Adobe Inc.
IPC: G06T5/00 , H04N5/232 , G06K9/46 , G06T15/50 , G06N3/08 , H04N5/235 , G06K9/00 , G06K9/62 , G06N3/04
Abstract: The present disclosure is directed toward systems and methods for predicting lighting conditions. In particular, the systems and methods described herein analyze a single low-dynamic range digital image to estimate a set of high-dynamic range lighting conditions associated with the single low-dynamic range lighting digital image. Additionally, the systems and methods described herein train a convolutional neural network to extrapolate lighting conditions from a digital image. The systems and methods also augment low-dynamic range information from the single low-dynamic range digital image by using a sky model algorithm to predict high-dynamic range lighting conditions.
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公开(公告)号:US20200186714A1
公开(公告)日:2020-06-11
申请号:US16789195
申请日:2020-02-12
Applicant: Adobe Inc.
IPC: H04N5/232 , G06K9/00 , G06N3/04 , G06K9/62 , G06K9/46 , G06T5/00 , H04N5/235 , G06N3/08 , G06T15/50
Abstract: The present disclosure is directed toward systems and methods for predicting lighting conditions. In particular, the systems and methods described herein analyze a single low-dynamic range digital image to estimate a set of high-dynamic range lighting conditions associated with the single low-dynamic range lighting digital image. Additionally, the systems and methods described herein train a convolutional neural network to extrapolate lighting conditions from a digital image. The systems and methods also augment low-dynamic range information from the single low-dynamic range digital image by using a sky model algorithm to predict high-dynamic range lighting conditions.
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公开(公告)号:US10609286B2
公开(公告)日:2020-03-31
申请号:US15621444
申请日:2017-06-13
Applicant: Adobe Inc.
IPC: G06T5/00 , H04N5/232 , G06K9/46 , G06T15/50 , G06N3/08 , H04N5/235 , G06K9/00 , G06K9/62 , G06N3/04
Abstract: The present disclosure is directed toward systems and methods for predicting lighting conditions. In particular, the systems and methods described herein analyze a single low-dynamic range digital image to estimate a set of high-dynamic range lighting conditions associated with the single low-dynamic range lighting digital image. Additionally, the systems and methods described herein train a convolutional neural network to extrapolate lighting conditions from a digital image. The systems and methods also augment low-dynamic range information from the single low-dynamic range digital image by using a sky model algorithm to predict high-dynamic range lighting conditions.
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