ADMM-SVNet: An ADMM-Based Sparse-View CT Reconstruction Network

Wang, Sukai; Li, Xuan; Chen, Ping

doi:10.3390/photonics9030186

Cited by 4 publications

(1 citation statement)

References 49 publications

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“…With the ability to penetrate most optically opaque materials and achieve non-ionizing radiation, microwave imaging [1,2] has many advantages over other modalities of imaging, such as CT [3], X-ray [4], MRI [5], particularly in security screening scenario [6]. Despite the superiority, there is still a challenge, that is, the need to synthesize composite apertures to scan the scene and acquire high resolution either mechanically or electronically.…”

Section: Introductionmentioning

confidence: 99%

Fast Frequency-Diverse Radar Imaging Based on Adaptive Sampling Iterative Soft-Thresholding Deep Unfolding Network

Zhao

Zhang

et al. 2023

Remote Sensing

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Frequency-diverse radar imaging is an emerging field that combines computational imaging with frequency-diverse techniques to interrogate the high-quality images of objects. Despite the success of deep reconstruction networks in improving scene image reconstruction from noisy or under-sampled frequency-diverse measurements, their reliance on large amounts of high-quality training data and the inherent uninterpretable features pose significant challenges in the design and optimization of imaging networks, particularly in the face of dynamic variations in radar operating frequency bands. Here, aiming at reducing the latency and processing burden involved in scene image reconstruction, we propose an adaptive sampling iterative soft-thresholding deep unfolding network (ASISTA-Net). Specifically, we embed an adaptively sampling module into the iterative soft-thresholding (ISTA) unfolding network, which contains multiple measurement matrices with different compressed sampling ratios. The outputs of the convolutional layers are then passed through a series of ISTA layers that perform a sparse coding step followed by a thresholding step. The proposed method requires no need for heavy matrix operations and massive amount of training scene targets and measurements datasets. Unlike recent work using matrix-inversion-based and data-driven deep reconstruction networks, our generic approach is directly adapted to multi-compressed sampling ratios and multi-scene target image reconstruction, and no restrictions on the types of imageable scenes are imposed. Multiple measurement matrices with different scene compressed sampling ratios are trained in parallel, which enables the frequency-diverse radar to select operation frequency bands flexibly. In general, the application of the proposed approach paves the way for the widespread deployment of computational microwave and millimeter wave frequency-diverse radar imagers to achieve real-time imaging. Extensive imaging simulations demonstrate the effectiveness of our proposed method.

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