GENERIC COMPRESSION RATIO ADAPTER FOR END-TO-END DATA-DRIVEN COMPRESSIVE SENSING RECONSTRUCTION FRAMEWORKS

    公开(公告)号:US20210305999A1

    公开(公告)日:2021-09-30

    申请号:US17218738

    申请日:2021-03-31

    IPC分类号: H03M7/30 G06N3/08 G06N3/04

    摘要: 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.

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    公开(公告)号:USD918136S1

    公开(公告)日:2021-05-04

    申请号:US29688231

    申请日:2019-04-19

    申请人: Kai Xu

    设计人: Kai Xu