Image super-resolution method, device and equipment and storage medium

A super-resolution and resolution technology, applied in the field of image processing, can solve the problems of a large amount of parameters and calculations, and the super-resolution network model cannot use features efficiently.

Pending Publication Date: 2021-01-05
SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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Problems solved by technology

[0004] The embodiment of the present application provides an image super-resolution method, device, device, and storage medium, which can solve the problem that the super-resolution network model in the related a

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  • Image super-resolution method, device and equipment and storage medium
  • Image super-resolution method, device and equipment and storage medium
  • Image super-resolution method, device and equipment and storage medium

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[0111] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0112] It should be understood that when used in this specification and the appended claims, the terms "comprising", "comprising", "having" and variations thereof mean "including but not limited to", unless otherwise stated With particular emphasis on. The terms "first", "second", "third", etc. are only used for distinguishing descriptions, and should not be construed as indicating or implying relative importance.

[0113] In related technologies, commonly used super-resolution network models are SRResNet or CARN, etc., but because these super-resolution network models ca...

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Abstract

The embodiment of the invention provides an image super-resolution method, device and equipment and a storage medium, and is suitable for the technical field of image processing. The method comprisesthe following steps: acquiring a first image; inputting the first image into a super-resolution network model to be processed, and obtaining a second image, wherein the resolution of the second imageis greater than that of the first image, the super-resolution network model is a network model adopting a pixel attention mechanism, and the pixel attention mechanism is used for generating a corresponding weight for each pixel in the input feature map. Due to the fact that the super-resolution network model adopts a pixel attention mechanism, the model can pay more attention to key features in the image processing process, the features can be utilized more efficiently, the parameter amount and the calculated amount required by the super-resolution network model are greatly reduced, and the processing speed of the super-resolution network model is increased. And the pixel attention mechanism is more suitable for bottom visual tasks such as super-resolution, so higher-quality visual experience can be provided for users while the super-resolution performance is improved.

Description

technical field [0001] The present application belongs to the technical field of image processing, and in particular relates to an image super-resolution method, device, equipment and storage medium. Background technique [0002] Super-resolution techniques are used to restore high-resolution images from a given low-resolution image. In general, the higher the image resolution, the more details and information contained in the image. In the field of electronic images, people often expect to obtain high-resolution images, but due to equipment, sensors, etc., the obtained images may be low-resolution images. In this case, it is necessary to use super-resolution technology to obtain Restore high-resolution images to meet people's needs for high-resolution images. [0003] In related technologies, a super-resolution method based on deep learning is usually used, that is, a super-resolution network model constructed based on deep learning is used to process low-resolution image...

Claims

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Application Information

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IPC IPC(8): G06T3/40G06K9/62G06N3/04
CPCG06T3/4053G06T3/4046G06N3/045G06F18/253
Inventor 董超乔宇赵恒远孔祥涛何静雯
Owner SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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