Unsupervised learning-based scale-independent blur kernel estimation for super-resolution
AssigneeSamsung Electronics Co., Ltd.
InventorsKamal Jnawali, Tien Cheng Bau
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Abstract
One embodiment provides a method generating a first image crop and a second image crop randomly extracted from a low-quality image and a high-quality image, respectively. The method further comprises comparing the first image crop and the second image crop using a plurality of loss functions including pixel-wise loss to calculate losses, and optimizing a model trained to estimate a realistic scale-independent blur kernel of a low-resolution (LR) blurred image by minimizing the losses.
Claims20
A method comprising:
- generating a first image crop comprising a first crop of a low-quality image that is randomly extracted from the low-quality image;
- generating a second image crop comprising a second crop of a high-quality image that is randomly extracted from the high-quality image, wherein the low-quality image and the high-quality image are different images having different image resolutions;
- comparing the first image crop and the second image crop using a plurality of loss functions including pixel-wise loss to calculate losses between the first image crop and the second image crop;
- optimizing a model by minimizing the losses between the first image crop and the second image crop;
- and
- estimating, utilizing the optimized model, a realistic scale-independent blur kernel of the low-quality image without different scaled versions of the low-quality image.
A system comprising:
- at least one processor;
- and
- a non-transitory processor-readable memory device storing instructions that when executed by the at least one processor causes the at least one processor to perform operations including:
- generating a first image crop comprising a first crop of a low-quality image that is randomly extracted from the low-quality image;
- generating a second image crop comprising a second crop of a high-quality image that is randomly extracted from the high-quality image, wherein the low-quality image and the high-quality image are different images having different image resolutions;
- comparing the first image crop and the second image crop using a plurality of loss functions including pixel-wise loss to calculate losses between the first image crop and the second image crop;
- optimizing a model by minimizing the losses between the first image crop and the second image crop;
- and
- estimating, utilizing the optimized model, a realistic scale-independent blur kernel of the low-quality image without different scaled versions of the low-quality image.
A non-transitory processor-readable medium that includes a program that when executed by a processor performs a method comprising:
- generating a first image crop comprising a first crop of a low-quality image that is randomly extracted from the low-quality image;
- generating a second image crop comprising a second crop of a high-quality image that is randomly extracted from the high-quality image, wherein the low-quality image and the high-quality image are different images having different image resolutions;
- comparing the first image crop and the second image crop using a plurality of loss functions including pixel-wise loss to calculate losses between the first image crop and the second image crop;
- optimizing a model by minimizing the losses between the first image crop and the second image crop;
- and
- estimating, utilizing the optimized model, a realistic scale-independent blur kernel of the low-quality image without different scaled versions of the low-quality image.
Dependencies are inferred from the claim text.