公开(公告)号：US11468542B2

公开(公告)日：2022-10-11

申请号：US16745817

申请日：2020-01-17

Applicant: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY

Inventor： Fengbo Ren ,  Kai Xu ,  Zhikang Zhang

IPC: G06T3/40 ,  G06T5/50 ,  G06N3/08 ,  G06N20/20 ,  G06N3/04

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.

公开(公告)号：US11777520B2

公开(公告)日：2023-10-03

申请号：US17218738

申请日：2021-03-31

Applicant: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY

Inventor： Zhikang Zhang ,  Fengbo Ren ,  Kai Xu

IPC: H03M7/34 ,  H03M7/30 ,  G06N3/08 ,  G06N3/045 ,  G06N3/084 ,  G06N20/20

CPC classification number: H03M7/6047 ,  G06N3/045 ,  G06N3/08 ,  G06N3/084 ,  G06N20/20

Abstract: A compression ratio (CR) adapter (CRA) for end-to-end data-driven compressive sensing (CS) reconstruction (EDCSR) frameworks is provided. EDCSR frameworks achieve state-of-the-art reconstruction performance in terms of reconstruction speed and accuracy for images and other signals. However, existing EDCSR frameworks cannot adapt to a variable CR. For applications that desire a variable CR, existing EDCSR frameworks must be trained from scratch at each CR, which is computationally costly and time-consuming. Embodiments described herein present a CRA framework that addresses the variable CR problem generally for existing and future EDCSR frameworks with no modification to given reconstruction models nor enormous additional rounds of training needed. The CRA exploits an initial reconstruction network to generate an initial estimate of reconstruction results based on a small portion of acquired image measurements. Subsequently, the CRA approximates full measurements for the main reconstruction network by complementing the sensed measurements with a re-sensed initial estimate.

公开(公告)号：US12211173B2

公开(公告)日：2025-01-28

申请号：US18045740

申请日：2022-10-11

Applicant: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY

Inventor： Fengbo Ren ,  Kai Xu ,  Zhikang Zhang

IPC: G06T3/4046 ,  G06N3/045 ,  G06N3/084 ,  G06N20/20 ,  G06T5/50

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.

公开(公告)号：US11763165B2

公开(公告)日：2023-09-19

申请号：US17317372

申请日：2021-05-11

Applicant: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY

Inventor： Zhikang Zhang ,  Fengbo Ren

IPC: G06N3/084 ,  G06F16/901 ,  G06N3/04 ,  G06N3/08 ,  G06F18/213 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06N3/084 ,  G06F16/901 ,  G06F18/213 ,  G06N3/04 ,  G06N3/08 ,  G06V10/764 ,  G06V10/82

Abstract: A data-driven nonuniform subsampling approach for computation-free on-sensor data dimensionality is provided, referred to herein as selective sensing. Designing an on-sensor data dimensionality reduction scheme for efficient signal sensing has long been a challenging task. Compressive sensing is a generic solution for sensing signals in a compressed format. Although compressive sensing can be directly implemented in the analog domain for specific types of signals, many application scenarios require implementation of data compression in the digital domain. However, the computational complexity involved in digital-domain compressive sensing limits its practical application, especially in resource-constrained sensor devices or high-data-rate sensor devices. Embodiments described herein provide a selective sensing framework that adopts a novel concept of data-driven nonuniform subsampling to reduce the dimensionality of acquired signals while retaining the information of interest in a computation-free fashion.