A Fast Spatial-domain Terahertz Imaging Using Block-based Compressed Sensing

Hwang, Byung-Min; Lee, Sang-Hun; Lim, Wootaek; Ahn, Chang‐Beom; Son, Joo‐Hiuk; Park, Hochong

doi:10.1007/s10762-011-9822-5

Cited by 17 publications

(11 citation statements)

References 14 publications

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“…For demonstrating its applicability to food quality inspection, a few food samples -such as crickets in noodle flour, and a chocolate bar -were successfully tested. In future work, if image processing algorithms and digital signal processing are optimized for enhanced image acquisition, much higher imaging speeds can be obtained [19][20][21]. By optimizing the collimating optics prior to the polygonal mirror, the beam size should be further minimized so that image quality can be improved.…”

Section: Resultsmentioning

confidence: 99%

High-performance sub-terahertz transmission imaging system for food inspection

Ok¹,

Park²,

Chun³

et al. 2015

Biomed. Opt. Express

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Unlike X-ray systems, a terahertz imaging system can distinguish low-density materials in a food matrix. For applying this technique to food inspection, imaging resolution and acquisition speed ought to be simultaneously enhanced. Therefore, we have developed the first continuous-wave sub-terahertz transmission imaging system with a polygonal mirror. Using an f-theta lens and a polygonal mirror, beam scanning is performed over a range of 150 mm. For obtaining transmission images, the line-beam is incorporated with sample translation. The imaging system demonstrates that a pattern with 2.83 mm line-width at 210 GHz can be identified with a scanning speed of 80 mm/s.

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Section: Resultsmentioning

confidence: 99%

High-performance sub-terahertz transmission imaging system for food inspection

Ok¹,

Park²,

Chun³

et al. 2015

Biomed. Opt. Express

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show abstract

“…For other comparative experiments, it only shows the average objective evaluations of CT-MRI, MRI-PET, and MRI-SPECT image fusion, respectively. In this paper, the quality of the proposed framework is compared with other existing methods, including BSD (Block-based Spatial Domain) [70], DWT [71], DT-CWT [72], CS-DCT [73] [27]. SRDL, JPC, and MST-SR used the similar frameworks as our proposed solution respectively to obtain good fusion performance.…”

Section: Methodsmentioning

confidence: 99%

An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework

Wang

Zhang

et al. 2017

Future Internet

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Abstract:Image fusion is widely used in different areas and can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. Medical image fusion, as an important image fusion application, can extract the details of multiple images from different imaging modalities and combine them into an image that contains complete and non-redundant information for increasing the accuracy of medical diagnosis and assessment. The quality of the fused image directly affects medical diagnosis and assessment. However, existing solutions have some drawbacks in contrast, sharpness, brightness, blur and details. This paper proposes an integrated dictionary-learning and entropy-based medical image-fusion framework that consists of three steps. First, the input image information is decomposed into low-frequency and high-frequency components by using a Gaussian filter. Second, low-frequency components are fused by weighted average algorithm and high-frequency components are fused by the dictionary-learning based algorithm. In the dictionary-learning process of high-frequency components, an entropy-based algorithm is used for informative blocks selection. Third, the fused low-frequency and high-frequency components are combined to obtain the final fusion results. The results and analyses of comparative experiments demonstrate that the proposed medical image fusion framework has better performance than existing solutions.

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“…The imaging speed depends on the specific image scheme chosen: using a point source and detector, the acquisition is slow, due to the point-by-point sample scanning. Although, the two-dimensional galvo-scanner, combined with a fast detector or sophisticated signal processing techniques [108][109][110], improves the scanning speed and the acquisition on a large field of view.…”

Section: Tpi Equipmentmentioning

confidence: 99%

THz Pulsed Imaging in Biomedical Applications

et al. 2020

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Recent advances in technology have allowed the production and the coherent detection of sub-ps pulses of terahertz (THz) radiation. Therefore, the potentialities of this technique have been readily recognized for THz spectroscopy and imaging in biomedicine. In particular, THz pulsed imaging (TPI) has rapidly increased its applications in the last decade. In this paper, we present a short review of TPI, discussing its basic principles and performances, and its state-of-the-art applications on biomedical systems.

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A Fast Spatial-domain Terahertz Imaging Using Block-based Compressed Sensing

Cited by 17 publications

References 14 publications

High-performance sub-terahertz transmission imaging system for food inspection

High-performance sub-terahertz transmission imaging system for food inspection

An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework

THz Pulsed Imaging in Biomedical Applications

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