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:
Oct. 24, 2023

Filed:

Dec. 20, 2019
Applicants:

Xiang Zhu, Santa Clara, CA (US);

Yuxin HU, Stanford, CA (US);

Mark Alan Duchaineau, Livermore, CA (US);

Google Llc, Mountain View, CA (US);

Inventors:

Xiang Zhu, Santa Clara, CA (US);

Yuxin Hu, Stanford, CA (US);

Mark Alan Duchaineau, Livermore, CA (US);

Assignee:

GOOGLE LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 3/40 (2006.01); G06T 5/00 (2006.01); G06T 5/20 (2006.01); G06V 10/56 (2022.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 10/774 (2022.01); H04N 9/64 (2023.01); H04N 23/88 (2023.01);
U.S. Cl.
CPC ...
H04N 9/646 (2013.01); G06T 3/40 (2013.01); G06T 5/002 (2013.01); G06T 5/20 (2013.01); G06V 10/56 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/7715 (2022.01); H04N 23/88 (2023.01); G06T 2207/10024 (2013.01); G06T 2207/10032 (2013.01); G06T 2207/20016 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30181 (2013.01);
Abstract

Methods, systems, devices, and tangible non-transitory computer readable media for haze reduction are provided. The disclosed technology can include generating feature vectors based on an input image including points. The feature vectors can correspond to feature windows associated with features of different portions of the points. Based on the feature vectors and a machine-learned model, a haze thickness map can be generated. The haze thickness map can be associated with an estimate of haze thickness at each of the points. Further, the machine-learned model can estimate haze thickness associated with the features. A refined haze thickness map can be generated based on the haze thickness map and a guided filter. A dehazed image can be generated based on application of the refined haze thickness map to the input image. Furthermore, a color corrected dehazed image can be generated based on performance of color correction operations on the dehazed image.


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