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:
Aug. 25, 2020

Filed:

Nov. 27, 2015
Applicant:

Canon Medical Systems Corporation, Otawara-shi, JP;

Inventors:

Zhou Yu, Wilmette, IL (US);

Qiulin Tang, Buffalo Grove, IL (US);

Satoru Nakanishi, Utsunomiya, JP;

Wenli Wang, Briarcliff Manor, NY (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 5/50 (2006.01); G06K 9/32 (2006.01); G06K 9/40 (2006.01); G06K 9/62 (2006.01); G06T 5/00 (2006.01); G06T 5/10 (2006.01); G06K 9/46 (2006.01);
U.S. Cl.
CPC ...
G06T 5/50 (2013.01); G06K 9/3233 (2013.01); G06K 9/40 (2013.01); G06K 9/6223 (2013.01); G06K 9/6247 (2013.01); G06K 9/6249 (2013.01); G06T 5/002 (2013.01); G06T 5/10 (2013.01); G06K 2009/4695 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10104 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20182 (2013.01); G06T 2207/20216 (2013.01); G06T 2207/20221 (2013.01); G06T 2207/30048 (2013.01);
Abstract

An apparatus and method of denoising a dynamic image is provided. The dynamic image can represent a time-series of snapshot images. The dynamic image is transformed, using a sparsifying transformation, into an aggregate image and a series of transform-domain images. The transform-domain images represent kinetic information of the dynamic images (i.e., differences between the snapshots), and the aggregate image represents static information (i.e., features and structure common among the snapshots). The transform-domain images, which can be approximated using a sparse approximation method, are denoised. The denoised transform-domain images are recombined with the aggregate image using an inverse sparsifying transformation to generate a denoised dynamic image. The transform-domain images can be denoised using at least one of a principal component analysis method and a K-SVD method.


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