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公开(公告)号：US11663481B2
公开(公告)日：2023-05-30
申请号：US16799191
申请日：2020-02-24
Applicant: Adobe Inc.
Inventor： Shikun Liu , Zhe Lin , Yilin Wang , Jianming Zhang , Federico Perazzi
Abstract: The disclosure describes one or more implementations of a neural network architecture pruning system that automatically and progressively prunes neural networks. For instance, the neural network architecture pruning system can automatically reduce the size of an untrained or previously-trained neural network without reducing the accuracy of the neural network. For example, the neural network architecture pruning system jointly trains portions of a neural network while progressively pruning redundant subsets of the neural network at each training iteration. In many instances, the neural network architecture pruning system increases the accuracy of the neural network by progressively removing excess or redundant portions (e.g., channels or layers) of the neural network. Further, by removing portions of a neural network, the neural network architecture pruning system can increase the efficiency of the neural network.
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公开(公告)号：US11521299B2
公开(公告)日：2022-12-06
申请号：US17072372
申请日：2020-10-16
Applicant: Adobe Inc.
Inventor： Federico Perazzi , Jingwan Lu
Abstract: The present disclosure relates to an image retouching system that automatically retouches digital images by accurately correcting face imperfections such as skin blemishes and redness. For instance, the image retouching system automatically retouches a digital image through separating digital images into multiple frequency layers, utilizing a separate corresponding neural network to apply frequency-specific corrections at various frequency layers, and combining the retouched frequency layers into a retouched digital image. As described herein, the image retouching system efficiently utilizes different neural networks to target and correct skin features specific to each frequency layer.
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Patent Agency Ranking
公开(公告)号：US20220156588A1
公开(公告)日：2022-05-19
申请号：US17590995
申请日：2022-02-02
Applicant: Adobe Inc.
Inventor： Federico Perazzi , Zhihao Xia , Michael Gharbi , Kalyan Sunkavalli
Abstract: Certain embodiments involve techniques for efficiently estimating denoising kernels for generating denoised images. For instance, a neural network receives a noisy reference image to denoise. The neural network uses a kernel dictionary of base kernels and generates a coefficient vector for each pixel in the reference image such that the coefficient vector includes a coefficient value for each base kernel in the kernel dictionary, where the base kernels are combined to generate a denoising kernel and each coefficient value indicates a contribution of a given base kernel to a denoising kernel. The neural network calculates the denoising kernel for a given pixel by applying the coefficient vector for that pixel to the kernel dictionary. The neural network applies each denoising kernel to the respective pixel to generate a denoised output image.
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公开(公告)号：US20220122224A1
公开(公告)日：2022-04-21
申请号：US17072372
申请日：2020-10-16
Applicant: Adobe Inc.
Inventor： Federico Perazzi , Jingwan Lu
Abstract: The present disclosure relates to an image retouching system that automatically retouches digital images by accurately correcting face imperfections such as skin blemishes and redness. For instance, the image retouching system automatically retouches a digital image through separating digital images into multiple frequency layers, utilizing a separate corresponding neural network to apply frequency-specific corrections at various frequency layers, and combining the retouched frequency layers into a retouched digital image. As described herein, the image retouching system efficiently utilizes different neural networks to target and correct skin features specific to each frequency layer.
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公开(公告)号：US11281970B2
公开(公告)日：2022-03-22
申请号：US16686978
申请日：2019-11-18
Applicant: Adobe Inc.
Inventor： Federico Perazzi , Zhihao Xia , Michael Gharbi , Kalyan Sunkavalli
Abstract: Certain embodiments involve techniques for efficiently estimating denoising kernels for generating denoised images. For instance, a neural network receives a noisy reference image to denoise. The neural network uses a kernel dictionary of base kernels and generates a coefficient vector for each pixel in the reference image such that the coefficient vector includes a coefficient value for each base kernel in the kernel dictionary, where the base kernels are combined to generate a denoising kernel and each coefficient value indicates a contribution of a given base kernel to a denoising kernel. The neural network calculates the denoising kernel for a given pixel by applying the coefficient vector for that pixel to the kernel dictionary. The neural network applies each denoising kernel to the respective pixel to generate a denoised output image.
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公开(公告)号：US11244204B2
公开(公告)日：2022-02-08
申请号：US16879362
申请日：2020-05-20
Applicant: Adobe Inc.
Inventor： Oliver Wang , Nico Alexander Becherer , Markus Woodson , Federico Perazzi , Nikhil Kalra
Abstract: In implementations of determining video cuts in video clips, a video cut detection system can receive a video clip that includes a sequence of digital video frames that depict one or more scenes. The video cut detection system can determine scene characteristics for the digital video frames. The video cut detection system can determine, from the scene characteristics, a probability of a video cut between two adjacent digital video frames having a boundary between the two adjacent digital video frames that is centered in the sequence of digital video frames. The video cut detection system can then compare the probability of the video cut to a cut threshold to determine whether the video cut exists between the two adjacent digital video frames.
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公开(公告)号：US20210365742A1
公开(公告)日：2021-11-25
申请号：US16879362
申请日：2020-05-20
Applicant: Adobe Inc.
Inventor： Oliver Wang , Nico Alexander Becherer , Markus Woodson , Federico Perazzi , Nikhil Kalra
Abstract: In implementations of determining video cuts in video clips, a video cut detection system can receive a video clip that includes a sequence of digital video frames that depict one or more scenes. The video cut detection system can determine scene characteristics for the digital video frames. The video cut detection system can determine, from the scene characteristics, a probability of a video cut between two adjacent digital video frames having a boundary between the two adjacent digital video frames that is centered in the sequence of digital video frames. The video cut detection system can then compare the probability of the video cut to a cut threshold to determine whether the video cut exists between the two adjacent digital video frames.
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