Style transfer-based image content correction
First Claim
1. An image processing method, comprising:
- obtaining a first source image;
extracting an artistic style from at least a portion of the first source image, wherein the extracted artistic style is stored as a plurality of layers in a convolutional neural network;
obtaining a first target image comprising one or more undesired artifacts;
obtaining semantic information from the first target image corresponding to the one or more undesired artifacts;
applying, using the plurality of layers stored in the convolutional neural network, the extracted artistic style to the first target image in accordance with the semantic information to repair the one or more undesired artifacts, thereby creating a content corrected version of the first target image; and
storing the content corrected version of the first target image in a memory.
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Abstract
Techniques are disclosed herein for applying an artistic style extracted from one or more source images, e.g., paintings, to one or more target images. The extracted artistic style may then be stored as a plurality of layers in a neural network. In some embodiments, two or more stylized target images may be combined and stored as a stylized video sequence. The artistic style may be applied to the target images in the stylized video sequence using various optimization methods and/or pixel- and feature-based regularization techniques in a way that prevents excessive content pixel fluctuations between images and preserves smoothness in the assembled stylized video sequence. In other embodiments, a user may be able to semantically annotate locations of undesired artifacts in a target image, as well as portion(s) of a source image from which a style may be extracted and used to replace the undesired artifacts in the target image.
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Citations
17 Claims
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1. An image processing method, comprising:
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obtaining a first source image; extracting an artistic style from at least a portion of the first source image, wherein the extracted artistic style is stored as a plurality of layers in a convolutional neural network; obtaining a first target image comprising one or more undesired artifacts; obtaining semantic information from the first target image corresponding to the one or more undesired artifacts; applying, using the plurality of layers stored in the convolutional neural network, the extracted artistic style to the first target image in accordance with the semantic information to repair the one or more undesired artifacts, thereby creating a content corrected version of the first target image; and storing the content corrected version of the first target image in a memory. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A non-transitory program storage device comprising instructions stored thereon to cause one or more processors to:
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obtain a first source image; extract an artistic style from at least a portion of the first source image, wherein the extracted artistic style is stored as a plurality of layers in a convolutional neural network; obtain a first target image comprising one or more undesired artifacts; obtain semantic information from the first target image corresponding to the one or more undesired artifacts; apply, using the plurality of layers stored in the convolutional neural network, the extracted artistic style to the first target image in accordance with the semantic information to repair the one or more undesired artifacts, thereby creating a content corrected version of the first target image; and store the content corrected version of the first target image in a memory. - View Dependent Claims (8, 9, 10, 11)
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12. A device, comprising:
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a memory; a user interface; one or more processors operatively coupled to the memory and the user interface, wherein the one or more processors are configured to execute instructions causing the one or more processors to; obtain a first source image; extract an artistic style from at least a portion of the first source image, wherein the extracted artistic style is stored as a plurality of layers in a convolutional neural network; obtain a first target image comprising one or more undesired artifacts; obtain semantic information from the first target image corresponding to the one or more undesired artifacts; apply, using the plurality of layers stored in the convolutional neural network, the extracted artistic style to the first target image in accordance with the semantic information to repair the one or more undesired artifacts, thereby creating a content corrected version of the first target image; and store the content corrected version of the first target image in the memory. - View Dependent Claims (13, 14, 15, 16, 17)
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Specification