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A Video Super-Resolution Reconstruction Method Based on Deep Dual Attention Network

An attention, low-resolution technology, applied in neural learning methods, biological neural network models, image enhancement, etc., can solve problems such as lack of recognition ability, and achieve the effect of achieving high performance, good restoration of image details, and excellent performance.

Active Publication Date: 2022-03-11
BEIJING JIAOTONG UNIV
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Problems solved by technology

Previous methods treat these information equally and lack the flexible recognition ability to modulate meaningful information for high-frequency detail recovery

Method used

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  • A Video Super-Resolution Reconstruction Method Based on Deep Dual Attention Network
  • A Video Super-Resolution Reconstruction Method Based on Deep Dual Attention Network
  • A Video Super-Resolution Reconstruction Method Based on Deep Dual Attention Network

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Embodiment Construction

[0113] Embodiments of the present invention will be described in detail below, and examples of the embodiments are illustrated in the drawings, in which the same or similar reference numerals are denoted by the same or similar elements or elements having the same or similar functions. By way of the following embodiments described with reference to the accompanying drawings are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0114] Those skilled in the art will appreciate that unless specifically declared, the singular form "one", "one", "" "and" "", and "" "can also include multiple forms. It should be further understood that the phrase "comprising" in the specification of the present invention means that there is the features, integers, steps, operations, elements, and / or components, but do not exclude presence or addition of one or more other features. Integral, steps, operations, components, components, and ...

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Abstract

A video super-resolution reconstruction method based on a deep dual attention network provided by the present invention, by loading a cascaded motion compensation network model and a reconstruction network model, fully utilizes spatio-temporal information features to achieve accurate video super-resolution reconstruction; wherein The motion compensation network model can gradually learn the optical flow representation and synthesize the multi-scale motion information of adjacent frames from rough to fine; in the reconstruction network model, the double attention mechanism is used to form a residual attention unit to focus on the intermediate information features , which can better recover image details; compared with the state-of-the-art, our method can effectively achieve superior performance in both quantitative and qualitative evaluations.

Description

Technical field [0001] The present invention relates to the field of video reconstruction, and more particularly to a video super-resolution reconstruction method based on a depth dual focus network. Background technique [0002] Video or multi-frame super resolution (SR) is a classic problem in image processing, and its goal is to generate a high resolution (HR) frame from a given low resolution (LR) video sequence. Video SR has been widely used in practical applications such as video surveillance, face hall illusion, video conversion. In the video SR problem, it is usually generated by different motion blur, down-sampling operation, and additive noise from the corresponding HR video to generate destroyed low quality LR video. We can observe that in the dynamics of the real world, we can perform super resolution for LR video, because there are many solutions to constrain the irreversible degradation of any LR input. In response to the SR problem, a variety of methods are propose...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T3/40G06T5/50G06N3/04G06N3/08
CPCG06T3/4053G06T5/50G06N3/08G06N3/044G06N3/045
Inventor 白慧慧李锋
Owner BEIJING JIAOTONG UNIV
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