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. 11, 2022

Filed:

Jan. 17, 2020
Applicant:

Arizona Board of Regents on Behalf of Arizona State University, Scottsdale, AZ (US);

Inventors:

Fengbo Ren, Tempe, AZ (US);

Kai Xu, Tempe, AZ (US);

Zhikang Zhang, Mesa, AZ (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 3/40 (2006.01); G06T 5/50 (2006.01); G06N 3/08 (2006.01); G06N 20/20 (2019.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06T 3/4046 (2013.01); G06N 3/0454 (2013.01); G06N 3/084 (2013.01); G06N 20/20 (2019.01); G06T 5/50 (2013.01); G06T 2207/20016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

This disclosure addresses the single-image compressive sensing (CS) and reconstruction problem. A scalable Laplacian pyramid reconstructive adversarial network (LAPRAN) facilitates high-fidelity, flexible and fast CS image reconstruction. LAPRAN progressively reconstructs an image following the concept of the Laplacian pyramid through multiple stages of reconstructive adversarial networks (RANs). At each pyramid level, CS measurements are fused with a contextual latent vector to generate a high-frequency image residual. Consequently, LAPRAN can produce hierarchies of reconstructed images and each with an incremental resolution and improved quality. The scalable pyramid structure of LAPRAN enables high-fidelity CS reconstruction with a flexible resolution that is adaptive to a wide range of compression ratios (CRs), which is infeasible with existing methods.


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