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A video super-resolution reconstruction method based on depth learning

A technology of super-resolution reconstruction and deep learning, applied in neural learning methods, image data processing, instruments, etc.

Active Publication Date: 2018-12-28
CHINA JILIANG UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0006] (1) SR is an inverse problem, and its difficulty lies in the recovery of details

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  • A video super-resolution reconstruction method based on depth learning
  • A video super-resolution reconstruction method based on depth learning
  • A video super-resolution reconstruction method based on depth learning

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Embodiment Construction

[0022] The specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0023] like image 3 As shown, the video super-resolution reconstruction method based on deep learning of the present invention comprises the following steps:

[0024] Step 1: Training and testing data preparation: (1) Collection of public data sets: Collect two sets of M pairs of public videos with the same content, one set is a low-resolution video, and the other set is a corresponding high-definition video; (2) Private data Set collection: use different mobile phones and different cameras to shoot high-definition videos, and collect N groups of high-definition videos in total, and use H i (i=1, 2, ... N) represent; The high-definition video H that will collect i Use an adversarial network to generate low-resolution videos with various camera motions, multiple scene depths, and multiple motion blurs, where the camera motion V i a (a=...

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Abstract

The invention discloses a video super-resolution reconstruction method based on depth learning. The technical key points are: (1) continuous images are given under the same shot, the network predictsclearer video frame images; (2) using a bi-directional circulating neural network and a depth 3D back projection network; (3) the invention combines two networks into one network, and the network is used as a network (4) for in-depth learning video super-resolution reconstruction of the invention. The training data is labeled, and the processed data video frames are passed through the network to obtain a loss function. A final object of the present invention is to input temporal and spatial information of a low-resolution video frame predicted by a bi-directional circulating network, after the3D projection network re-predicts the details of the video frame, an optimal model is obtained after repeated training. The model is applied to remove the effects of the camera shake, the blur of theobject's fast motion, out-of-focus blur, lens optical blur, depth-of-field variation, compression distortion and noise, and other degradation factors.

Description

technical field [0001] The invention belongs to the field of video processing, in particular to a video super-resolution reconstruction method based on deep learning. Background technique [0002] Video super-resolution (super resolution, SR) is the process of obtaining high-resolution video from a low-resolution video. This technology is mainly used to enhance the spatial resolution of the video, which can break through the limitations of the original system imaging hardware conditions. Due to limitations, the re-obtained high-resolution video has higher resolution, more detailed information, and higher-quality picture quality. It is currently one of the most effective and cheapest ways to obtain high-resolution video. [0003] In the process of video acquisition, limited by factors such as imaging conditions and imaging methods, the imaging system usually cannot acquire all the information in the original scene, and it will be affected by many factors such as vibration, de...

Claims

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Application Information

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IPC IPC(8): G06T3/40G06N3/04G06N3/08
CPCG06N3/084G06T3/4046G06T3/4053G06N3/045Y02T10/40
Inventor 章东平张香伟倪佩青
Owner CHINA JILIANG UNIV
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