paper-with-me

Papers

Sub-diffraction terahertz backpropagation compressive imaging

2025-05-05 · Yongsheng Zhu, Shaojing Liu, Ximiao Wang, Runli Li, Haili Yang, Jiali Wang, Hongjia Zhu, Yanlin Ke, Ningsheng Xu, Huanjun Chen, Shaozhi Deng

Terahertz single-pixel imaging (TSPI) has garnered significant attention due to its simplicity and cost-effectiveness. However, the relatively long wavelength of THz waves limits sub-diffraction-scale imaging resolution. Although TSPI technique can achieve sub-wavelength resolution, it requires harsh experimental conditions and time-consuming processes. Here, we propose a sub-diffraction THz backpropagation compressive imaging technique. We illuminate the object with monochromatic continuous-wave THz radiation. The transmitted THz wave is modulated by prearranged patterns generated on the back surface of a 500-{\mu}m-thick silicon wafer, realized through photoexcited carriers using a 532-nm laser. The modulated THz wave is then recorded by a single-element detector. An untrained neural network is employed to iteratively reconstruct the object image with an ultralow compression ratio of 1.5625% under a physical model constraint, thus reducing the long sampling times. To further suppress the diffraction-field effects, embedded with the angular spectrum propagation (ASP) theory to model the diffraction of THz waves during propagation, the network retrieves near-field information from the object, enabling sub-diffraction imaging with a spatial resolution of ~{\lambda}0/7 ({\lambda}0 = 833.3 {\mu}m at 0.36 THz) and eliminating the need for ultrathin photomodulators. This approach provides an efficient solution for advancing THz microscopic imaging and addressing other inverse imaging challenges.

📄 PDF Abstract BibTeX arXiv:2505.07839

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
Attention 설명 없음

Similar Papers 제목 키워드 기반

A Parallel Compressive Imaging Architecture for One-Shot Acquisition

2013-11-04 · Tomas Björklund, Enrico Magli

A limitation of many compressive imaging architectures lies in the sequential nature of the sensing process, which leads to long sensing times. In this paper we present a novel architecture that uses fewer detectors than…

Compressive Sensing

Generative adversarial network for super-resolution imaging through a fiber

2022-01-03 · Wei Li, Ksenia Abrashitova, Gerwin Osnabrugge, Lyubov V. Amitonova

A multimode fiber represents the ultimate limit in miniaturization of imaging endoscopes. Here we propose a fiber imaging approach employing compressive sensing with a data-driven machine learning framework. We implement…

Compressive SensingGenerative Adversarial NetworkImage ReconstructionSuper-Resolution

Parallel compressive super-resolution imaging with wide field-of-view based on physics enhanced network

2023-10-20 · Xiao-Peng Jin, An-Dong Xiong, Wei zhang, Xiao-Qing Wang 외

Achieving both high-performance and wide field-of-view (FOV) super-resolution imaging has been attracting increasing attention in recent years. However, such goal suffers from long reconstruction time and huge storage sp…

Super-Resolution

Compressive Sensing Imaging Using Caustic Lens Mask Generated by Periodic Perturbation in a Ripple Tank

2024-05-01 · Doğan Tunca Arık, Asaf Behzat Şahin, Özgün Ersoy

Terahertz imaging shows significant potential across diverse fields, yet the cost-effectiveness of multi-pixel imaging equipment remains an obstacle for many researchers. To tackle this issue, the utilization of single-p…

Compressive SensingImage Generation

Multispectral Compressive Imaging Strategies using Fabry-Pérot Filtered Sensors

2018-02-06 · Kévin Degraux, Valerio Cambareri, Bert Geelen, Laurent Jacques 외

This paper introduces two acquisition device architectures for multispectral compressive imaging. Unlike most existing methods, the proposed computational imaging techniques do not include any dispersive element, as they…

compressed sensingSuper-Resolution