• Proceedings of the KSRS Conference
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• 2003.11a
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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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• v.14 no.4
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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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• v.52 no.11
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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.

• Journal of radiological science and technology
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• v.45 no.2
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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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• v.24 no.2E
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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.

• Proceedings of the KSME Conference
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• 2004.11a
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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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• v.25 no.4E
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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.

• Journal of Broadcast Engineering
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• v.9 no.2
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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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• v.19 no.1_2
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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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.85-93
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• 2007

In case that multichannel blind deconvolution (MBD) applies to signals of which autocorrelation has a high level, separated signals are temporally whitened by diagonal elements of a separation filter matrix. In order to reduce this distortion, the algorithms, which are based on either constraining diagonal elements of a separation filter matrix or estimating a separation filter matrix by using linear prediction residual signals, are presented. Still, some problems are generated in these methods, when we separate reverberation of signals themselves or interference signals from mixed signals. To solve these problems, this paper proposes the multichannel blind deconvolution method which divides processing procedure into the stage to separate interference signals and the stage to eliminate a reverberation of signals themselves. In simulation results, we confirm that the proposed algorithm can solve the problems.