• ETRI Journal
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• v.41 no.3
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• pp.316-325
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• 2019

Accurate suppressive jamming is a prominent problem faced by radar equipment. It is difficult to solve signal detection problems for extremely low signal to noise ratios using traditional signal processing methods. In this study, a joint sensing dictionary based compressed sensing and adaptive iterative optimization algorithm is proposed to counter suppressive jamming in information domain. Prior information of the linear frequency modulation (LFM) and suppressive jamming signals are fully used by constructing a joint sensing dictionary. The jamming sensing dictionary is further adaptively optimized to perfectly match actual jamming signals. Finally, through the precise reconstruction of the jamming signal, high detection precision of the original LFM signal is realized. The construction of sensing dictionary adopts the Pei type fast fractional Fourier decomposition method, which serves as an efficient basis for the LFM signal. The proposed adaptive iterative optimization algorithm can solve grid mismatch problems brought on by undetermined signals and quickly achieve higher detection precision. The simulation results clearly show the effectiveness of the method.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.505-508
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• 2000

The success of target reconstruction in SAR(Synthetic Aperture Radar) imaging system is greatly dependent on the coherent detection. Primary causes of incoherent detection are uncompensated target or sensor motion, random turbulence in propagation media, wrong path in radar platform, and etc. And these appear as multiplicative phase error to the echoed signal, which consequently, causes fatal degradations such as fading or dislocation of target image. In this paper, we present iterative phase error estimation scheme which uses echoed data in all temporal frequencies. We started with analyzing wave equation for one point target and extend to overall echoed data from the target scene - The two wave equations governing the SAR signal at two temporal frequencies of the radar signal are combined to derive a method to reconstruct the complex phase error function. Eventually, this operation attains phase error correction algorithm from the total received SAR signal. We verify the success of the proposed algorithm by applying it to the simulated spotlight-mode SAR data.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.309-312
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• 2016

최근 실세계에 존재하는 물체의 3차원 형상과 색상을 디지털화하는 3차원 객체 복원에 대한 관심이 날로 증가하고 있다. 3차원 객체 복원은 영상 획득, 영상 보정, 점군 획득, 반복적 점군 정합, 무리 조정, 3차원 모델 표현과 같은 단계를 거처 통합된 3차원 모델을 생성한다. 그 중 반복적 점군 정합 방법은 카메라 궤적의 초기 값을 획득하는 방법으로서 무리 조정 단계에서 전역 최적 값으로의 수렴을 보장하기 위해 중요한 단계이다. 기존의 반복적 점군 정합 (iterative closest points) 방법에서는 시간이 지남에 따라 누적된 궤적 오차 때문에 발생하는 객체 표류 문제가 발생한다. 본 논문에서는 이 문제를 해결하기 위해 색상 영상에서 SIFT 특징점을 획득하고 3차원 점군을 얻은 뒤 가중치를 부여함으로써 점 군 간의 더 정확한 정합을 수행한다. 실험결과에서 기존의 방법과 비교하여 제안하는 방법이 절대 궤적 오차 (absolute trajectory error)가 감소하는 것을 확인 했고 복원된 3차원 모델에서 객체 표류 현상이 줄어드는 것을 확인했다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.26-33
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• 1994

Phase retrieval is concerned with the reconstruction of a signal from its Fourier transform magnitude (or intensity), which arises in many areas such as X-ray crystallography, optics, astronomy, or digital signal processing. In such areas, the Fourier transform phase of the desired signal is lost while measuring Fourier transform magnitude (F.T.M.). However, if a reference 'signal is added to the desired signal, then, in the Fourier trans form magnitude of the added signal, the Fourier transform phase of the desired signal is encoded. This paper addresses uniqueness and retrieval of the encoded Fourier phase of a multidimensional signal from the Fourier transform magnitude of the added signal along with the Fourier transform magnitude of the desired signal and the information of the additive reference signal. In Part I, several conditions under which the desired signal can be uniquely specified from the two Fourier transform magnitudes and the additive reference signal are presented. In Part II, the development of non-iterative algorithms and an iterative algorithm that may be used to reconstruct the desired signal(s) is considered.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.173-176
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• 2001

Several dielectric as well as conducting cylinders buried in the lossy half space are reconstructed from the scattered fields measured along the interface between the air and the lossy ground. Iterative inversion method by using the hybrid optimization algorithm combining the genetic and the Levenberg-Marquardt algorithm enables us to find the positions, the sizes, and the medium parameters such as the permittivities and the conductivities of the buried cylinders as well as those of the background lossy half space. Illposedness of the inversion caused by the errors in the measured scattered fields are regularized by filtering the evanescent modes of the scattered fields out.

• Korean Journal of Radiology
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• v.22 no.1
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• pp.131-138
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• 2021

Objective: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). Materials and Methods: This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. Results: Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). Conclusion: DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.84-91
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• 2017

This study was performed using Somatom Definition Flash (Siemens, Enlarge, Germany) and GE 64-MDCT (Discovery 750 HD, GE HEALTHCARE, Milwaukee, USA.) using high-density medical materials that (are indispensable to?) computed tomography. We analyzed quantitatively the rate of change of the CT number of the CT reconstruction images by means of the IMAR and MAR algorithms using the phantom images acquired after scanning and previously captured raw data images. As a result, it was shown that the IMAR and MAR algorithms provided if ferent phantom images in the case of all medical high-density materials (p <0.05). The black streak artifacts were analyzed using the MAR and IMAR algorithms to determine if they corresponded to stainless steel materials (p>0.05). Also, it was found that the application of the IMAR algorithm affects the attenuation deviation, because there is a change in the image CT number compared to that before. The results suggest that, in the future, after the implant procedure, it would be useful to observe the surgical site and surrounding tissues during follow-up CT scans.

• Journal of the Korean Society of Radiology
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• v.15 no.5
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• pp.603-612
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• 2021

The effect of Advanced Modeled Iterative Reconstruction (ADMIRE) on the coronary artery calcium (CAC) score of computed tomography was evaluated. Coronary artery calcium images (348 calcium, 6 groups, total of 2088 calcium) were acquired by 128-slice dual-source CT of 89 patients.Volume score and Agatston score were measured from images reconstructed with filtered back projection (FBP) and ADMIRE (1-5). The difference between FBP and ADMIRE Strength (1-5) was confirmed through the Kruskal-Wallis test, and the post-hoc analysis was performed using the Mann-Whitney U test based on FBP. Both volume score and Agatston score showed statistically significant differences between FBP and ADMIRE (1-5) (P=0.015, P=0.0.38). As a result of post hoc analysis, the volume score decreased to 9.5% in ADMIRE 4 (Z=-2.359, P=0.018) and 13.2% in ADMIRE 5 (Z=-3.113, P=0.002) based on FBP. Agatston score decreased to 10.4% in ADMIRE 4 (Z=-2.051, P=0.040) and 14.0% in ADMIRE 5 (Z=-2.718, P=0.007) based on FBP. High ADMIRE strength affected the volume score and Agatston score due to the decrease in calcium area. In addition, the change in the Density factor due to the decrease in Maximum HU may affect the calculation of the Agatston score.

• Korean Journal of Radiology
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• v.22 no.6
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• pp.983-993
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• 2021

Objective: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods: Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 ± 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.

• Korean Journal of Artificial Intelligence
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• v.9 no.2
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• pp.1-5
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• 2021

Although CT has an advantage in describing the three-dimensional anatomical structure of the human body, it also has a disadvantage in that high doses are exposed to the patient. Recently, a deep learning-based image reconstruction method has been used to reduce patient dose. The purpose of this study is to analyze the dose reduction and image quality improvement of deep learning-based reconstruction (DLR) on the adult's chest CT examination. Adult lung phantom was used for image acquisition and analysis. Lung phantom was scanned at ultra-low-dose (ULD), low-dose (LD), and standard dose (SD) modes, and images were reconstructed using FBP (Filtered back projection), IR (Iterative reconstruction), DLR (Deep learning reconstruction) algorithms. Image quality variations with respect to varying imaging doses were evaluated using noise and SNR. At ULD mode, the noise of the DLR image was reduced by 62.42% compared to the FBP image, and at SD mode, the SNR of the DLR image was increased by 159.60% compared to the SNR of the FBP image. Based on this study, it is anticipated that the DLR will not only substantially reduce the chest CT dose but also drastic improvement of the image quality.