公开(公告)号：US20220392025A1

公开(公告)日：2022-12-08

申请号：US17338949

申请日：2021-06-04

Applicant: Adobe Inc.

Inventor： Ionut Mironica ,  Yijun Li

IPC: G06T5/00 ,  G06T7/11 ,  G06T3/00 ,  G06T5/50 ,  G06N3/04 ,  G06N3/08

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly restoring degraded digital images utilizing a deep learning framework for repairing local defects, correcting global imperfections, and/or enhancing depicted faces. In particular, the disclosed systems can utilize a defect detection neural network to generate a segmentation map indicating locations of local defects within a digital image. In addition, the disclosed systems can utilize an inpainting algorithm to determine pixels for inpainting the local defects to reduce their appearance. In some embodiments, the disclosed systems utilize a global correction neural network to determine and repair global imperfections. Further, the disclosed systems can enhance one or more faces depicted within a digital image utilizing a face enhancement neural network as well.

公开(公告)号：US20210358092A1

公开(公告)日：2021-11-18

申请号：US15930995

申请日：2020-05-13

Applicant: Adobe Inc.

Inventor： Ionut Mironica ,  Oscar Bolaños ,  Andreea Birhalä

IPC: G06T5/00 ,  G06T7/00 ,  G06K9/62 ,  G06T7/33 ,  G06T5/50

Abstract: In implementations of correcting dust and scratch artifacts in digital images, an artifact correction system receives a digital image that depicts a scene and includes a dust or scratch artifact. The artifact correction system generates, with a generator of a generative adversarial neural network (GAN), a feature map from the digital image that represents features of the dust or scratch artifact and features of the scene. A training system can train the generator adversarially to reduce visibility of dust and scratch artifacts in digital images against a discriminator, and train the discriminator to distinguish between reconstructed digital images generated by the generator and real-world digital images. The artifact correction system generates, from the feature map and with the generator, a reconstructed digital image that depicts the scene of the digital image and reduces visibility of the dust or scratch artifact of the digital image.