Hybrid Sparsity Model for Fast Terahertz Imaging

Ren, Xiaozhen; Bai, Yanwen; Jiang, Yuying

doi:10.3390/mi12101181

Cited by 2 publications

(3 citation statements)

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“…where peakval denotes the peak value of the terahertz image, and MSE(x, x) denotes the mean square error. To evaluate the proposed GSNS method, we compare the GSNS with the single sparse constraint algorithm (SSC) [17], dual sparsity constraint algorithm (DSC) [18] and the hybrid sparsity model (HSM) [22]. In the experiment, all the methods were compared using the same observation matrix.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…These methods use the nonlocal self-similarity of image patches to improve the image quality. A hybrid sparsity model was proposed for terahertz imaging in [22], which utilized the local sparsity and nonlocal self-similarity of the terahertz image to improve image quality, but it does not use the image structure information. Recently, in order to remove noise and reconstruct the image more effectively, methods based on group sparsity have been widely used in image processing, which can extract sparse information from the image structure [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Fast Terahertz Imaging Model Based on Group Sparsity and Nonlocal Self-Similarity

et al. 2022

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In order to solve the problems of long-term image acquisition time and massive data processing in a terahertz time domain spectroscopy imaging system, a novel fast terahertz imaging model, combined with group sparsity and nonlocal self-similarity (GSNS), is proposed in this paper. In GSNS, the structure similarity and sparsity of image patches in both two-dimensional and three-dimensional space are utilized to obtain high-quality terahertz images. It has the advantages of detail clarity and edge preservation. Furthermore, to overcome the high computational costs of matrix inversion in traditional split Bregman iteration, an acceleration scheme based on conjugate gradient method is proposed to solve the terahertz imaging model more efficiently. Experiments results demonstrate that the proposed approach can lead to better terahertz image reconstruction performance at low sampling rates.

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Section: Experiments and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast Terahertz Imaging Model Based on Group Sparsity and Nonlocal Self-Similarity

et al. 2022

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“…Deconvolution methods enhance THz image resolution and suppress noise based on the accurate modelling of the point spread function 23 . Compressed sensing techniques have also been widely investigated in THz image reconstruction 18,[26][27][28][29] . As compressed sensing is able to reconstruct images from relatively few measurements by the exploitation of sparsity, it has been demonstrated effective for high-speed THz imaging, like single-pixel THz imaging systems 28,29 .…”

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Deep learning for terahertz image denoising in nondestructive historical document analysis

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Ullmann

et al. 2022

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Historical documents contain essential information about the past, including places, people, or events. Many of these valuable cultural artifacts cannot be further examined due to aging or external influences, as they are too fragile to be opened or turned over, so their rich contents remain hidden. Terahertz (THz) imaging is a nondestructive 3D imaging technique that can be used to reveal the hidden contents without damaging the documents. As noise or imaging artifacts are predominantly present in reconstructed images processed by standard THz reconstruction algorithms, this work intends to improve THz image quality with deep learning. To overcome the data scarcity problem in training a supervised deep learning model, an unsupervised deep learning network (CycleGAN) is first applied to generate paired noisy THz images from clean images (clean images are generated by a handwriting generator). With such synthetic noisy-to-clean paired images, a supervised deep learning model using Pix2pixGAN is trained, which is effective to enhance real noisy THz images. After Pix2pixGAN denoising, 99% characters written on one-side of the Xuan paper can be clearly recognized, while 61% characters written on one-side of the standard paper are sufficiently recognized. The average perceptual indices of Pix2pixGAN processed images are 16.83, which is very close to the average perceptual index 16.19 of clean handwriting images. Our work has important value for THz-imaging-based nondestructive historical document analysis.

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Hybrid Sparsity Model for Fast Terahertz Imaging

Cited by 2 publications

References 30 publications

Fast Terahertz Imaging Model Based on Group Sparsity and Nonlocal Self-Similarity

Fast Terahertz Imaging Model Based on Group Sparsity and Nonlocal Self-Similarity

Deep learning for terahertz image denoising in nondestructive historical document analysis

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