Super-resolution restoration method based on monitoring video

A monitoring video and super-resolution technology, applied in the field of object-based super-resolution restoration algorithm, can solve the problem of low image resolution, achieve the effect of improving computing efficiency and reducing computing workload

Inactive Publication Date: 2019-06-28
TIANJIN UNIV
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Problems solved by technology

In view of the limitations of video surveillance technology in various aspects such as storage and system cost, in many

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  • Super-resolution restoration method based on monitoring video
  • Super-resolution restoration method based on monitoring video
  • Super-resolution restoration method based on monitoring video

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Embodiment Construction

[0015] The super-resolution restoration algorithm based on surveillance video of the present invention mainly consists of three parts: reading and preprocessing of surveillance video, moving target and segmentation, and object-based super-resolution restoration algorithm. The specific steps and principles are as follows:

[0016] 1. Reading and preprocessing of surveillance video;

[0017] Use Matlab software to read the video and divide the video into continuous sequence image form.

[0018] Perform Gaussian blur processing on the sequence image frame and down-sample to reduce the original image to a quarter of the original image. In the object-based super-resolution restoration, the simulated low-resolution image needs to be used as input; and the moving region of interest needs to be segmented out for subsequent restoration work.

[0019] 2. Moving target detection and segmentation;

[0020] The purpose of moving object detection is to find out the moving area in the for...

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Abstract

The invention relates to a super-resolution restoration method based on a monitoring video. The method comprises the following steps: (1) reading and preprocessing a monitoring video: reading the video by applying Matlab software, dividing the video into a form of continuous sequence images, performing Gaussian blur processing on the obtained sequence images, performing down-sampling, and synthesizing the down-sampled sequence images into video frames to simulate and obtain a reduced-quality low-resolution video; and (2) performing moving object detection and segmentation: detecting a moving object by using a three-frame difference method, designing a minimum bounding box, extracting the moving object, and enabling the sizes of output images to be consistent; and (3) only providing high-resolution information for the moving target, namely separating the moving target from the background, adopting a convex set projection method for the moving target, and directly performing bilinear interpolation processing on the background.

Description

technical field [0001] The invention belongs to the field of super-resolution image restoration algorithms, is applied to actual scenes of monitoring video, and proposes an object-based super-resolution restoration algorithm, which can effectively improve calculation efficiency. Background technique [0002] With the advent of the digital age, along with the rapid development and wide application of science and technology, video surveillance technology has become more and more mature, and has also been more widely used. However, at this stage, it is limited by various aspects such as storage, cost, and communication. In many cases, the image resolution obtained by the front-end acquisition and camera equipment is low, which cannot meet the needs of actual occasions. If you want to obtain more detailed monitoring information, you must improve the corresponding hardware level to obtain images with higher resolution. However, it is expensive to upgrade the hardware to increase...

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Application Information

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IPC IPC(8): H04N7/01H04N5/14H04N5/262H04N7/18G06T7/215
Inventor 段子阳李锵关欣
Owner TIANJIN UNIV
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