Invention Grant
- Patent Title: Utilizing an image exposure transformation neural network to generate a long-exposure image from a single short-exposure image
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Application No.: US16984992Application Date: 2020-08-04
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Publication No.: US11544831B2Publication Date: 2023-01-03
- Inventor: Yilin Wang , Zhe Lin , Zhaowen Wang , Xin Lu , Xiaohui Shen , Chih-Yao Hsieh
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: Keller Preece PLLC
- Main IPC: G06T5/50
- IPC: G06T5/50 ; G06K9/00 ; G06K9/62 ; G03B7/00

Abstract:
The present disclosure relates to training and utilizing an image exposure transformation network to generate a long-exposure image from a single short-exposure image (e.g., still image). In various embodiments, the image exposure transformation network is trained using adversarial learning, long-exposure ground truth images, and a multi-term loss function. In some embodiments, the image exposure transformation network includes an optical flow prediction network and/or an appearance guided attention network. Trained embodiments of the image exposure transformation network generate realistic long-exposure images from single short-exposure images without additional information.
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