Video deblurring method based on motion vector and CNN

A motion vector and deblurring technology, which is applied in image data processing, instrumentation, computing, etc., can solve problems such as inability to repair blurred parts of video frames, and achieve the effect of efficient video blur repair, clear video repair results, and small time complexity

Active Publication Date: 2019-08-09
杭州电子科技大学上虞科学与工程研究院有限公司 +1
View PDF12 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although it can denoise the video, the repair of the video screen is the entire video file, and it cannot repair the blurred part of the video frame

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Video deblurring method based on motion vector and CNN
  • Video deblurring method based on motion vector and CNN
  • Video deblurring method based on motion vector and CNN

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0045] This embodiment provides a video deblurring method based on motion vectors and CNN, such as figure 1 shown, including steps:

[0046] S11. processing all video frames of the video to be repaired in blocks to generate several image blocks;

[0047] S12. Perform blur detection on several image blocks of the video frame to obtain blurred image blocks to be repaired;

[0048] S13. Search for a clear image block corresponding to an adjacent frame of the blurred image block to be repaired;

[0049] S14. Input the blurred image block to be repaired and the clear image block into the CNN deblurring network;

[0050] S15. The CNN deblurring network generates a deblurring result;

[0051] S16. Complete the deblurring of the video to be repaired.

[0052] In step S11, all video frames of the video to be repaired are divided into blocks to generate several image blocks.

[0053] Specifically, each video frame of the video to be repaired is divided into blocks, wherein the size...

Embodiment 2

[0081] This embodiment provides a video deblurring method based on motion vectors and CNN, such as figure 2 shown, including steps:

[0082] S11. processing all video frames of the video to be repaired in blocks to generate several image blocks;

[0083] S12. Perform blur detection on several image blocks of the video frame to obtain blurred image blocks to be repaired;

[0084] S13. Search for a clear image block corresponding to an adjacent frame of the blurred image block to be repaired;

[0085] S131. Find an optimal clear image block of the clear image block corresponding to the adjacent frame.

[0086] S14. Input the blurred image block to be repaired and the optimal clear image block into the CNN deblurring network;

[0087] S15. The CNN deblurring network generates a deblurring result;

[0088] S16. Complete the deblurring of the video to be repaired.

[0089] The specific process is as Figure 8 shown.

[0090] In step S11, all video frames of the video to be ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a video deblurring method based on a motion vector and a CNN, and the method comprises the steps: S11, carrying out the block processing of all video frames of a to-be-restoredvideo, and generating a plurality of image blocks; S12, performing fuzzy detection on a plurality of image blocks of the video frame to obtain fuzzy image blocks to be restored; S13, searching a clear image block corresponding to an adjacent frame of the to-be-restored blurred image block; S14, inputting the blurred image block to be restored and the clear image block into a CNN deblurring network; S15, the CNN deblurring network generating a deblurring result; and S16, completing the deblurring of the to-be-restored video. According to the method, the effect of efficient video blurring restoration is achieved, the method is different from the mode that deblurring is conducted on the whole video frame, and only the blurred part in the video frame is restored. In addition, the advantages of the CNN in the aspect of computer vision processing are further utilized, and a clear video restoration result is obtained.

Description

technical field [0001] The present invention relates to the technical field of video blurring, in particular to a video deblurring method based on motion vectors and CNN. Background technique [0002] Most of the information obtained by people is observed through the human visual system, and images and videos, as the main visual information, have gradually become one of the main ways of information transmission in life, enriching the way of information exchange between people It is not limited to language and written communication. Moreover, images and videos are needed in many fields. For example, medical images help to understand the patient's condition; remote sensing images help to monitor changes in the environment; video surveillance is widely used in traffic intersections, railway stations, campuses, etc., ensuring security of the city. However, different types of distortion may be introduced in the process of video shooting and transmission, resulting in quality de...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06T7/238
CPCG06T5/003G06T5/005G06T2207/10016G06T2207/20081G06T2207/20084G06T7/238
Inventor 张善卿李黎陆剑锋骆挺
Owner 杭州电子科技大学上虞科学与工程研究院有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products