Missing region prediction
AssigneeAdobe Systems Incorporated
InventorsXin Lu, Zhe Lin, Jen-Chan Jeff Chien
Life of this patent
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Abstract
Missing region prediction techniques are described. In implementations, an image pair is obtained that includes first and second images. The first image is corrupted by removing a region of content, resulting in a corrupted image having a missing region. The corrupted image and the second image of the image pair are then used to generate a training-image pair. Then, based on a plurality of training-image pairs including the generated training-image pair, a model is trained using machine learning. The model can subsequently be used to predict pixel values of pixels within a subsequent missing region of a subsequent image that is not used as part of the training.
Claims20
In a digital medium environment to predict pixel values of a missing region in an image, a method implemented by at least one computing device, the method comprising:
- obtaining, by the at least one computing device, an image pair including first and second images;
- corrupting, by the at least one computing device, the first image by removing a region of content to produce a corrupted first image having a missing region;
- generating, by the at least one computing device, a training-image pair including the corrupted first image and the second image of the image pair;
- and
- training, by the at least one computing device, a model using machine learning on a plurality of training-image pairs including the training-image pair, the model trained to predict pixel values of pixels within a subsequent missing region of a subsequent image that is not used as part of the training.
In a digital medium environment to predict pixel values of a missing region in an image, a method implemented by at least one processor, the method comprising:
- obtaining a plurality of image pairs, each image pair including an original image and a corrupted image that is a corrupted version of the original image, the corrupted image having at least one region of missing content;
- training, by the at least one processor, a model using pixel-wise end-to-end machine learning on the plurality of images pairs based on the corrupted image as input and the original image as output, the model trained to identify content corresponding to the missing content and restore the corrupted image to the original image;
- and
- generating, based on the model, initial predicted pixel values corresponding to pixels within a subsequent missing region of a subsequent corrupted image that is independent of the plurality of image pairs.
In a digital medium environment to predict content of a missing region in an image to enhance results of a content aware fill operation, a system comprising:
- at least one processor;
- and
- at least one computer-readable storage media storing instructions that are executable by the at least one processor to implement a similarity module configured to:
- obtain an image having an empty region lacking content;
- apply a neural network to the image to predict content for the empty region, the neural network trained using machine learning on a plurality of image pairs independent of the image, each said image pair having an original image and a corrupted image that is a corrupted version of the original image;
- identify at least one additional region, in the image, that includes content having visual similarities to the predicted content in the empty region;
- and
- generate prediction results that map the content from the at least one additional region to the empty region.
Dependencies are inferred from the claim text.