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US 12,646,141 B2Granted
Image processing apparatus and image processing method thereof
AssigneeSamsung Electronics Co., Ltd.
InventorsCheon Lee, Donghyun Kim, Yongsup Park, Jaeyeon Park, Iljun Ahn, Hyunseung Lee, Taegyoung Ahn, Youngsu Moon, Tammy Lee
Life of this patent
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Abstract
An image processing apparatus applies an image to a first learning network model to optimize the edges of the image, applies the image to a second learning network model to optimize the texture of the image, and applies a first weight to the first image and a second weight to the second image based on information on the edge areas and the texture areas of the image to acquire an output image.
Claims20
An image processing apparatus comprising:
- a memory storing at least one instruction;
- and
- a processor configured to execute the at least one instruction stored in the memory to:
- obtain a first image in which a first area is processed and a first image characteristic is enhanced in an input image by applying the input image to a first neural network model,
- obtain a second image in which a second area is processed and a second image characteristic is enhanced in the input image by applying the input image to a second neural network model,
- obtain a first weight corresponding to the first image and a second weight corresponding to the second image by applying the input image to a third neural network model, and
- provide an output image by mixing the first and second images according to the first weight and the second weight,
- wherein each of the first neural network model and the second neural network model is trained to enhance different characteristics of an image.
5 more elements
A method of controlling an image processing apparatus, the method comprising:
- obtaining a first image in which a first area is processed and a first image characteristic is enhanced in an input image by applying the input image to a first neural network model;
- obtaining a second image in which a second area is processed and a second image characteristic is enhanced in the input image by applying the input image to a second neural network model;
- obtaining a first weight corresponding to the first image and a second weight corresponding to the second image by applying the input image to a third neural network model, and
- providing an output image by mixing the first and second images according to the first weight and the second weight,
- wherein each of the first neural network model and the second neural network model is trained to enhance different characteristics of an image.
2 more elements
Dependencies are inferred from the claim text.