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:
Jul. 21, 2026

Filed:

Jan. 30, 2023
Applicant:

Shenzhen Hypernano Optics Technology Co., Ltd., Shenzhen, CN;

Inventors:

Jinbiao Huang, Shenzhen, CN;

Xingchao Yu, Shenzhen, CN;

Zhe Ren, Shenzhen, CN;

Bin Guo, Shenzhen, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G01N 21/27 (2006.01); G06T 7/11 (2017.01); G06T 7/80 (2017.01); G06T 7/90 (2017.01); G06V 10/25 (2022.01); G06V 10/56 (2022.01); G06V 10/58 (2022.01); H04N 23/88 (2023.01); G01J 3/28 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0002 (2013.01); G01N 21/27 (2013.01); G06T 7/0004 (2013.01); G06T 7/11 (2017.01); G06T 7/80 (2017.01); G06T 7/90 (2017.01); G06V 10/25 (2022.01); G06V 10/56 (2022.01); G06V 10/58 (2022.01); H04N 23/88 (2023.01); G01J 3/28 (2013.01); G06T 2207/10036 (2013.01);
Abstract

A method for performing point-to-point whiteboard parameter ratio correction on a hyperspectral image includes: capturing hyperspectral data of a standard reference whiteboard in advance as white(x,y,w), and storing the records, then captures hyperspectral data of a sample as sample(x,y,w); then selecting an unobstructed and unshaded whiteboard area within a certain range of the hyperspectral data sample(x,y,w) of the test sample, and labeling the area as Area A; calculating a spectral average of the ROI on the sample image as S(w); calculating a spectral average of whiteboard data in the same position as the ROI as W(w); dividing the two spectral averages, and obtaining a correction coefficient alpha(w)=W(w)./S(w); multiplying the whiteboard parameter ratio correction coefficient alpha(w) by a sample reflectance image matrix after whiteboard parameter ratio correction to obtain a final hyperspectral reflectance image matrix REFL(x,y,w)=alpha(w).*sample(x,y,w)./white(x,y,w).


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