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Video super-resolution reconstruction method based on deep dual attention network

A super-resolution reconstruction and attention technology, applied in neural learning methods, biological neural network models, image data processing, etc., can solve problems such as lack of recognition ability

Active Publication Date: 2020-04-07
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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  • Video super-resolution reconstruction method based on deep dual attention network
  • Video super-resolution reconstruction method based on deep dual attention network
  • Video super-resolution reconstruction method based on deep dual attention network

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Embodiment Construction

[0113] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures 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 understand that unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the word "comprising" used in the description of the present invention refers to the presence of said features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, Integers, steps, operations, elements, components, and / or groups thereof. It will be understoo...

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Abstract

According to the video super-resolution reconstruction method based on the deep dual attention network provided by the invention, the cascaded motion compensation network model and reconstruction network model are loaded, and the accurate video super-resolution reconstruction is realized by fully utilizing the spatial-temporal information characteristics, wherein the motion compensation network model can gradually learn the multi-scale motion information of the optical flow representation synthesis adjacent frames from rough to fine; a double attention mechanism is utilized in a reconstructionnetwork model, a residual attention unit is formed, intermediate information features are focused, and image details can be better recovered; and compared with the prior art, the method can effectively realize excellent performance in the aspects of quantitative and qualitative evaluation.

Description

technical field [0001] The invention relates to the technical field of video reconstruction, in particular to a video super-resolution reconstruction method based on a deep double attention network. Background technique [0002] Video or multi-frame super-resolution (SR) is a classic problem in image processing, where the goal is to generate high-resolution (HR) frames from a given low-resolution (LR) video sequence. Video SR has been widely used in practical applications such as video surveillance, face hallucination, and video transformation. In video SR problems, it is common to generate corrupted low-quality LR videos from the corresponding HR videos by various motion blurring, downsampling operations, and additive noise. We can observe that super-resolution for LR video is an ill-posed problem in real-world dynamics, since there are many solutions that constrain the irreversible degradation of any LR input. Aiming at the SR problem, people have proposed a variety of m...

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Application Information

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Patent Type & Authority Applications(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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