公开(公告)号：US20210067782A1

公开(公告)日：2021-03-04

申请号：US16556744

申请日：2019-08-30

Applicant: Adobe Inc.

Inventor： Saumitra Sharma ,  Sunil Kumar

IPC: H04N19/132 ,  H04N19/33 ,  H04N19/625 ,  H04N19/80 ,  H04N19/176 ,  H04N19/119

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating digital images of modified resolution by filtering in the frequency domain. For example, the disclosed systems can utilize a tiling procedure to generate discrete cosine transform blocks. The disclosed systems can further filter the quantized data of the discrete cosine transform blocks within the frequency domain using, for example, a Lanczos resampling kernel. In addition, the digital image resolution modification system can utilize sub-band approximation and block composition to generate a modified digital image.

公开(公告)号：US11102487B2

公开(公告)日：2021-08-24

申请号：US16556744

申请日：2019-08-30

Applicant: Adobe Inc.

Inventor： Saumitra Sharma ,  Sunil Kumar

IPC: G06K9/00 ,  H04N19/132 ,  H04N19/33 ,  H04N19/119 ,  H04N19/80 ,  H04N19/176 ,  H04N19/625

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating digital images of modified resolution by filtering in the frequency domain. For example, the disclosed systems can utilize a tiling procedure to generate discrete cosine transform blocks. The disclosed systems can further filter the quantized data of the discrete cosine transform blocks within the frequency domain using, for example, a Lanczos resampling kernel. In addition, the digital image resolution modification system can utilize sub-band approximation and block composition to generate a modified digital image.

公开(公告)号：US20240005578A1

公开(公告)日：2024-01-04

申请号：US18467397

申请日：2023-09-14

Applicant: Adobe Inc.

Inventor： Saikat Chakrabarty ,  Sunil Kumar

IPC: G06T11/60 ,  G06N3/08 ,  G06T5/50 ,  G06V40/16

CPC classification number: G06T11/60 ,  G06N3/08 ,  G06T5/50 ,  G06V40/165 ,  G06V40/171

Abstract: The disclosure describes one or more embodiments of systems, methods, and non-transitory computer-readable media that generate a transferred hairstyle image that depicts a person from a source image having a hairstyle from a target image. For example, the disclosed systems utilize a face-generative neural network to project the source and target images into latent vectors. In addition, in some embodiments, the disclosed systems quantify (or identify) activation values that control hair features for the projected latent vectors of the target and source image. Furthermore, in some instances, the disclosed systems selectively combine (e.g., via splicing) the projected latent vectors of the target and source image to generate a hairstyle-transfer latent vector by using the quantified activation values. Then, in one or more embodiments, the disclosed systems generate a transferred hairstyle image that depicts the person from the source image having the hairstyle from the target image by synthesizing the hairstyle-transfer latent vector.

公开(公告)号：US20220101577A1

公开(公告)日：2022-03-31

申请号：US17034845

申请日：2020-09-28

Applicant: Adobe Inc.

Inventor： Saikat Chakrabarty ,  Sunil Kumar

IPC: G06T11/60 ,  G06K9/00 ,  G06T5/50 ,  G06N3/08

Abstract: The disclosure describes one or more embodiments of systems, methods, and non-transitory computer-readable media that generate a transferred hairstyle image that depicts a person from a source image having a hairstyle from a target image. For example, the disclosed systems utilize a face-generative neural network to project the source and target images into latent vectors. In addition, in some embodiments, the disclosed systems quantify (or identify) activation values that control hair features for the projected latent vectors of the target and source image. Furthermore, in some instances, the disclosed systems selectively combine (e.g., via splicing) the projected latent vectors of the target and source image to generate a hairstyle-transfer latent vector by using the quantified activation values. Then, in one or more embodiments, the disclosed systems generate a transferred hairstyle image that depicts the person from the source image having the hairstyle from the target image by synthesizing the hairstyle-transfer latent vector.

公开(公告)号：US11790581B2

公开(公告)日：2023-10-17

申请号：US17034845

申请日：2020-09-28

Applicant: Adobe Inc.

Inventor： Saikat Chakrabarty ,  Sunil Kumar

IPC: G06T11/60 ,  G06K9/00 ,  G06T5/50 ,  G06N3/08 ,  G06V40/16

CPC classification number: G06T11/60 ,  G06N3/08 ,  G06T5/50 ,  G06V40/165 ,  G06V40/171

Abstract: The disclosure describes one or more embodiments of systems, methods, and non-transitory computer-readable media that generate a transferred hairstyle image that depicts a person from a source image having a hairstyle from a target image. For example, the disclosed systems utilize a face-generative neural network to project the source and target images into latent vectors. In addition, in some embodiments, the disclosed systems quantify (or identify) activation values that control hair features for the projected latent vectors of the target and source image. Furthermore, in some instances, the disclosed systems selectively combine (e.g., via splicing) the projected latent vectors of the target and source image to generate a hairstyle-transfer latent vector by using the quantified activation values. Then, in one or more embodiments, the disclosed systems generate a transferred hairstyle image that depicts the person from the source image having the hairstyle from the target image by synthesizing the hairstyle-transfer latent vector.

公开(公告)号：US11763436B2

公开(公告)日：2023-09-19

申请号：US16944452

申请日：2020-07-31

Applicant: ADOBE INC.

Inventor： Saikat Chakrabarty ,  Sunil Kumar

IPC: G06T7/00 ,  G06T5/50 ,  G06N3/084 ,  G06V10/75

CPC classification number: G06T7/0002 ,  G06N3/084 ,  G06T5/50 ,  G06V10/751 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20104 ,  G06T2207/20212

Abstract: Systems and methods for digital image processing are described. Embodiments of the systems and methods includes identifying a digital image that includes an obscured region; generating a predicted image using a neural network comprising an encoder, a feature transfer network, and a decoder, wherein the feature transfer network is trained to predict features corresponding to the obscured region based on an output of the decoder; and combining the digital image and the predicted image to produce a reconstructed version of the digital image including details corresponding to the obscured region that are not present in the digital image.