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公开(公告)号：US10810707B2
公开(公告)日：2020-10-20
申请号：US16204675
申请日：2018-11-29
Applicant: Adobe Inc.
Inventor： Jianming Zhang , Zhe Lin , Xiaohui Shen , Oliver Wang , Lijun Wang
Abstract: Techniques of generating depth-of-field blur effects on digital images by digital effect generation system of a computing device are described. The digital effect generation system is configured to generate depth-of-field blur effects on objects based on focal depth value that defines a depth plane in the digital image and a aperture value that defines an intensity of blur effect applied to the digital image. The digital effect generation system is also configured to improve the accuracy with which depth-of-field blur effects are generated by performing up-sampling operations and implementing a unique focal loss algorithm that minimizes the focal loss within digital images effectively.
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公开(公告)号：US20200175651A1
公开(公告)日：2020-06-04
申请号：US16204675
申请日：2018-11-29
Applicant: Adobe Inc.
Inventor： Jianming Zhang , Zhe Lin , Xiaohui Shen , Oliver Wang , Lijun Wang
Abstract: Techniques of generating depth-of-field blur effects on digital images by digital effect generation system of a computing device are described. The digital effect generation system is configured to generate depth-of-field blur effects on objects based on focal depth value that defines a depth plane in the digital image and a aperture value that defines an intensity of blur effect applied to the digital image. The digital effect generation system is also configured to improve the accuracy with which depth-of-field blur effects are generated by performing up-sampling operations and implementing a unique focal loss algorithm that minimizes the focal loss within digital images effectively.
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公开(公告)号：US20200175700A1
公开(公告)日：2020-06-04
申请号：US16204785
申请日：2018-11-29
Applicant: Adobe Inc.
Inventor： Jianming Zhang , Zhe Lin , Xiaohui Shen , Oliver Wang , Lijun Wang
Abstract: Joint training technique for depth map generation implemented by depth prediction system as part of a computing device is described. The depth prediction system is configured to generate a candidate feature map from features extracted from training digital images, generate a candidate segmentation map and a candidate depth map from the generated candidate feature map, and jointly train portions of the depth prediction system using a loss function. Consequently, depth prediction system is able to generate a depth map that identifies depths of objects using ordinal depth information and accurately delineates object boundaries within a single digital image.
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