• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.58.4-58.4
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• 2019

We present the application of the Point Spread Function (PSF) deconvolution method to the astronomical Integral Field Unit (IFU) Spectroscopy data focus on the restoration of the galaxy kinematics. We apply the Lucy-Richardson deconvolution algorithm to the 2D image at each wavelength slice. We make a set of mock IFU data which resemble the IFU observation to the model galaxies with a diverse combination of surface brightness profile, S/N, line-of-sight geometry and Line-Of-Sight Velocity Distribution (LOSVD). Using the mock IFU data, we demonstrate that the algorithm can effectively recover the stellar kinematics of the galaxy. We also show that lambda_R_e, the proxy of the spin parameter can be correctly measured from the deconvolved IFU data. Implementation of the algorithm to the actual SDSS-IV MaNGA IFU survey data exhibits the noticeable difference on the 2D LOSVD, geometry, lambda_R_e. The algorithm can be applied to any other regular-grid IFS data to extract the PSF-deconvolved spatial information.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2006.08a
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• pp.66-66
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• 2006

• 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.

• 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 Korean Information Science Society Conference
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• 1998.10c
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• pp.336-338
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• 1998

This paper presents a novel neural network structure to the blind deconvolution task where the input (source) to a system is not available and the source has any type of distribution including sparse distribution. We employ multiple sensors so that spatial information plays a important role. The resulting learning algorithm is linear so that it works for both sub-and super-Gaussian source. Moreover, we can successfully deconvolve the mixture of a sparse source, while most existing algorithms [5] have difficulties in this task. Computer simulations confirm the validity and high performance of the proposed algorithm.

• Journal of Biomedical Engineering Research
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• v.5 no.1
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• pp.39-46
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• 1984

In order to improve the performance of ultrasonic diagnostic equipment, it is important to development the signal processing considering the ultrasonic properties of biological medium and propagation mechanism in tissue. Attenuation coefficient is not only important factor to analyze propagation properties, but also it is significant to estimate it in view of tissue characterization, so we show one of the method to estimate attenuation coefficient of biological tissue and the results of estimation.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.753-756
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• 2000

In this paper we present and compare two different spatio-temporal decorrelation learning algorithms for updating the weights of a linear feedforward network with FIR synapses (MIMO FIR filter). Both standard gradient and the natural gradient are employed to derive the spatio-temporal decorrelation algorithms. These two algorithms are applied to multichannel blind deconvolution task and their performance is compared. The rigorous derivation of algorithms and computer simulation results are presented.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.6
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• pp.609-617
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• 2020

Non-destructive inspection using X-rays is used as a method to check the inside of products. In order to accurately inspect, a X-ray image requires a higher spatial resolution. However, the reduction in pixel size of the X-ray detector, which determines the spatial resolution, is time-consuming and expensive. In this regard, a DMM has been proposed to obtain an improved spatial resolution using the same X-ray detector. However, this has a limitation that the motion blur phenomenon, which is a decrease in spatial resolution. In this paper, motion blur was removed by applying Non-Blind Deconvolution to the DMM image, and the increase in spatial resolution was confirmed. DMM and Non-Blind Deconvolution were sequentially applied to X-ray images, confirming 62 % MTF value by an additional 29 % over 33 % of DMM only. In addition, SSIM and PSNR were compared to confirm the similarity to the 1/2 pixel detector image through 0.68 and 33.21 dB, respectively.

• Journal of the Optical Society of Korea
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• v.15 no.3
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• pp.252-263
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• 2011

In this work, for the first time, the quality of restoration in wide-field microscopy images after deconvolution was analyzed as a function of different Point Spread Functions using one deconvolution method, on a specimen of known size and on a biological specimen. The empirical Point Spread Function determination can significantly depend on the numerical aperture, refractive index of the embedding medium, refractive index of the immersion oil and cover slip thickness. The influence of all of these factors is shown in the same article and using the same microscope. We have found that the best deconvolution results are obtained when the empirical PSF utilized is obtained under the same conditions as the specimen. We also demonstrated that it is very important to quantitatively check the process' outcome using several quality indicators: Full-Width at Half-Maximum, Contrast-to-Noise Ratio, Signal-to-Noise Ratio and a Tenengrad-based function. We detected a significant improvement when using an indicator to measure the focus of the whole stack. Therefore, to qualitatively determinate the best deconvolved image between different conditions, one approach that we are pursuing is to use Tenengrad-based function indicators in images obtained using a wide-field microscope.

• Geophysics and Geophysical Exploration
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• v.10 no.1
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• pp.44-49
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• 2007

It is difficult to obtain high-resolution images by 3D gravity inversion, because the problem is extremely underdetermined - there are too many model parameters. In order to reduce the number of model parameters we propose a 3D gravity inversion scheme utilising Euler deconvolution as a priori information. The essential point of this scheme is the reduction of the nonuniqueness of solutions by restricting the inversion space with the help of Euler deconvolution. We carry out a systematic exploration of the growing body process, but only in the restricted space within a certain radius of the Euler solutions. We have tested our method with synthetic gravity data, and also applied it to a real dataset, to delineate underground cavities in a limestone area. We found that we obtained a more reasonable subsurface density image by means of this combination between the Euler solution and the inversion process.