• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.673-675
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• 2003

The image restoration algorithm for microwave imaging radiometer is proposed. A blind deconvolution method was proposed. A point spread function was identified and three deconvolution schemes were employed, Wiener filtering, Lucy- Richardson deconvolution, and Maximum Likelihood blind deconvolution. The experimental data is illustrated with restored image.

• JSTS:Journal of Semiconductor Technology and Science
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• 제14권4호
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• pp.365-375
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• 2014

This paper proposes an abnormal V-shaped-error-free non-blind deconvolution technique featuring an adaptively segmented forward-problem based iterative deconvolution (ASDCN) process. Unlike the algebraic based inverse operations, this eliminates any operations of differential and division by zero to successfully circumvent the issue on the abnormal V-shaped error. This effectiveness has been demonstrated for the first time with applying to a real analysis for the effects of the Random Telegraph Noise (RTN) and/or Random Dopant Fluctuation (RDF) on the overall SRAM margin variations. It has been shown that the proposed ASDCN technique can reduce its relative errors of RTN deconvolution by $10^{13}$ to $10^{15}$ fold, which are good enough for avoiding the abnormal ringing errors in the RTN deconvolution process. This enables to suppress the cdf error of the convolution of the RTN with the RDF (i.e., fail-bit-count error) to $1/10^{10}$ error for the conventional algorithm.

• Nuclear Engineering and Technology
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• 제52권11호
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• pp.2594-2600
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• 2020

A gamma camera system using radionuclide has a functional imaging technique and is frequently used in the field of nuclear medicine. In the gamma camera, it is extremely important to improve the image quality to ensure accurate detection of diseases. In this study, we designed a blind-deconvolution framework after a noise-reduction algorithm based on a non-local mean, which has been shown to outperform conventional methodologies with regard to the gamma camera system. For this purpose, we performed a simulation using the Monte Carlo method and conducted an experiment. The image performance was evaluated by visual assessment and according to the intensity profile, and a quantitative evaluation using a normalized noise-power spectrum was performed on the acquired image and the blind-deconvolution image after noise reduction. The result indicates an improvement in image performance for gamma camera images when our proposed algorithm is used.

• 대한방사선기술학회지:방사선기술과학
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• 제45권2호
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• pp.145-150
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• 2022

The main purpose of this work was to restore the blurry chest CT images by applying a blind deconvolution algorithm. In general, image restoration is the procedure of improving the degraded image to get the true or original image. In this regard, we focused on a blind deblurring approach with chest CT imaging by using digital image processing in MATLAB, which the blind deconvolution technique performed without any whole knowledge or information as to the fundamental point spread function (PSF). For our approach, we acquired 30 chest CT images from the public source and applied three type's PSFs for finding the true image and the original PSF. The observed image might be convolved with an isotropic gaussian PSF or motion blurring PSF and the original image. The PSFs are assumed as a black box, hence restoring the image is called blind deconvolution. For the 30 iteration times, we analyzed diverse sizes of the PSF and tried to approximate the true PSF and the original image. For improving the ringing effect, we employed the weighted function by using the sobel filter. The results was compared with the three criteria including mean squared error (MSE), root mean squared error (RMSE) and peak signal-to-noise ratio (PSNR), which all values of the optimal-sized image outperformed those that the other reconstructed two-sized images. Therefore, we improved the blurring chest CT image by using the blind deconvolutin algorithm for optimal approach.

• The Journal of the Acoustical Society of Korea
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• 제24권2E호
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• pp.54-60
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• 2005

A new multichannel blind deconvolution algorithm is proposed for speech mixtures. It employs unidirectional filters and normalization of gradient terms in the frequency domain. The proposed algorithm is shown to be approximately nonholonomic. Thus it provides improved convergence and separation performances without whitening effect for nonstationary sources such as speech and audio signals. Simulations using real world recordings confirm superior performances over existing algorithms and its usefulness for real applications.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.585-590
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• 2004

Many machine failures are not detected well in advance due to the masking of background noise and attenuation of the source signal through the transmission mediums. Advanced signal processing techniques using adaptive filters and higher order statistics have been attempted to extract the source signal from the measured data at the machine surface. In this paper, blind deconvolution using the eigenvector algorithm (EVA) technique is used to recover a damaged bearing signal using only the measured signal at the machine surface. A damaged bearing signal corrupted by noise with varying signal-to-noise (s/n) was used to determine the effectiveness of the technique in detecting an incipient signal and the optimum choice of filter length. The results show that the technique is effective in detecting the source signal with an s/n ratio as low as 0.21, but requires a relatively large filter length.

• The Journal of the Acoustical Society of Korea
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• 제25권4E호
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• pp.148-153
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• 2006

A new normalized MBD algorithm is presented for nonstationary convolutive mixtures and its properties/modifications are discussed in details. The proposed algorithm normalizes the signal spectrum in the frequency domain to provide faster stable convergence and improved separation without whitening effect. Modifications such as nonholonomic constraints and off-diagonal learning to the proposed algorithm are also discussed. Simulation results using a real-world recording confirm superior performanceof the proposed algorithm and its usefulness in real world applications.

• 방송공학회논문지
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• 제9권2호
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• pp.110-118
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• 2004

This paper shows that blind deconvolution has only finite solutions when an original image and a point spread function are nonzero over a restricted domain, in other words, an observed image has a compact support. The key of the proof is to use z-transformations and factorizations of polynomials. Then, we propose an algorithm to find all finite solutions under the boundary condition. Finally, we confirm that we can extract all sets of an original image and a point spread function from a degraded image by using our algorithm in numerical examples.

• Journal of applied mathematics & informatics
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• 제19권1_2호
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• pp.151-164
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• 2005

The regularized structured total least norm (RSTLN) method finds an approximate solution x and error matrix E to the overdetermined linear system (H + E)x $\approx$ b, preserving structure of H. A new separation scheme by parts of variables for the regularized structured total least norm on blind deconvolution problem is suggested. A method combining the regularized structured total least norm method with a separation by parts of variables can be obtain a better approximated solution and a smaller residual. Computational results for the practical problem with Block Toeplitz with Toeplitz Block structure show the new method ensures more efficiency on image restoration.

• 대한전자공학회논문지SP
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• 제44권1호
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• pp.85-93
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• 2007

다채널 암묵 디콘볼루션을 자기상관 값이 큰 신호에 적용할 경우 분리필터행렬의 주대각 성분에 의해서 분리신호의 시간백색화가 발생한다. 이러한 왜곡을 줄이기 위해 분리필터 행렬의 주대각 성분을 강제하거나 선형예측 잔여신호를 이용하여 분리필터 행렬을 구하는 방법들이 제안되었지만 신호자신의 반향성분이나 간섭신호 분리에 있어서 문제점이 발생된다. 본 논문에서는 이러한 문제들을 해결하기 위해서 간섭신호의 분리를 위한 단계와 신호자신의 반향을 감소시키기 위한 단계를 분리하여 처리하는 구조의 다채널 암묵 디콘볼루션 방법을 제안한다. 모의실험 결과 혼합신호에서 간섭신호를 분리해낼 수 있을 뿐만아니라 신호 자신의 반향 또한 감소됨을 확인하였다.