The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 01, 2022

Filed:

Aug. 17, 2020
Applicant:

Nanjing University of Posts and Telecommunications, Jiangsu, CN;

Inventors:

Dengyin Zhang, Nanjing, CN;

Chao Zhou, Jiangsu, CN;

Can Chen, Jiangsu, CN;

Junjiang Li, Jiangsu, CN;

Zijie Liu, Jiangsu, CN;

Assignee:

Other;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H04N 19/176 (2014.01); G06F 17/16 (2006.01); H04N 19/90 (2014.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
H04N 19/90 (2014.11); G06F 17/16 (2013.01); G06N 3/08 (2013.01); H04N 19/176 (2014.11);
Abstract

The present disclosure provides a deep neural network (DNN)-based reconstruction method and apparatus for compressive video sensing (CVS). The method divides a video signal into a key frame and a non-key frame. The key frame is reconstructed by using an existing image reconstruction method. The non-key frame is reconstructed by using a special DNN according to the present disclosure. The neural network includes an adaptive sampling module, a multi-hypothesis prediction module, and a residual reconstruction module. The neural network makes full use of a spatio-temporal correlation of the video signal to sample and reconstruct the video signal. This ensures low time complexity of an algorithm while improving reconstruction quality. Therefore, the method in the present disclosure is applicable to a video sensing system with limited resources on a sampling side and high requirements for reconstruction quality and real-time performance.


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Zhang, Dengyin. (2022). Deep neural network (dnn)-based reconstruction method and apparatus for compressive video sensing (cvs) (U.S. Patent No. 11490128). U.S. Patent and Trademark Office. https://idiyas.com/patent/badge/11490128

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