• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.34-41
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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 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

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4160-4176
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• 2015

Data compression like image and video compression has come a long way since the introduction of Compressive Sensing (CS) which compresses sparse signals such as images, videos etc. to very few samples i.e. M < N measurements. At the receiver end, a robust and efficient recovery algorithm estimates the original image or video. Many prominent algorithms solve least squares problem (LSP) iteratively in order to reconstruct the signal hence consuming more processing time. In this paper non-iterative threshold based recovery algorithm (NITRA) is proposed for the recovery of images and videos without solving LSP, claiming reduced complexity and better reconstruction quality. The elapsed time for images and videos using NITRA is in ㎲ range which is 100 times less than other existing algorithms. The peak signal to noise ratio (PSNR) is above 30 dB, structural similarity (SSIM) and structural content (SC) are of 99%.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.43.2-43.2
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• 2019

Tomography is a method to reconstruct three-dimensional structure of an optically thin object. We can obtain the three-dimensional information by combining a number of projected images at different angles. Solar rotational tomography (SRT) is the tomographic method to estimate the coronal structures using the solar rotation. There are a few practical difficulties in solar coronal observation. One of the most crucial difficulty is handling the blocking area by the occulter or the Sun itself. So we have to use the iterative reconstruction for the SRT which can resolve that problem by using the forward modeling. In this study, we propose an alternative method to reconstruct the solar coronal structure: the filtered backprojection (FBP) algorithm. The FBP algorithm is based on the simple analytic solution. Thus it is easy to understand, and the computing cost is much cheaper than that of the iterative reconstruction. Recently we found a solution for the FBP algorithm to the problem of the blocking area in the solar EUV observations. We introduce how to apply the FBP algorithm to the SRT, and show the initial results of the performance test.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.83-90
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• 2017

Electrical impedance tomography is a relatively new nondestructive imaging modality in which the internal conductivity distribution is reconstructed based on the injected currents and measured voltages through electrodes placed on the surface of a domain. In this paper, a fast iterative Gauss-Newton method is proposed to increase the spatial resolution as well as reduce the inverse computational time in the inverse problem, which could be applied to online binary mixture flow applications. To evaluate the reconstruction performance of the proposed method, numerical experiments have been carried out and the results are analyzed.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.219-226
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• 2017

Penalized-likelihood (PL) reconstruction methods for transmission tomography are known to provide improved image quality for reduced dose level by efficiently smoothing out noise while preserving edges. Unfortunately, however, most of the edge-preserving penalty functions used in conventional PL methods contain at least one free parameter which controls the shape of a non-quadratic penalty function to adjust the sensitivity of edge preservation. In this work, to avoid difficulties in finding a proper value of the free parameter involved in a non-quadratic penalty function, we propose a new adaptive method of space-variant smoothing with a simple quadratic penalty function. In this method, the smoothing parameter is adaptively selected for each pixel location at each iteration by using the image roughness measured by a pixel-wise standard deviation image calculated from the previous iteration. The experimental results demonstrate that our new method not only preserves edges, but also suppresses noise well in monotonic regions without requiring additional processes to select free parameters that may otherwise be included in a non-quadratic penalty function.

• Proceedings of the Korean Society of Medical Physics Conference
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• 1999.11a
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• pp.121-123
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• 1999

• Nuclear Medicine and Molecular Imaging
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• v.42 no.2
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• pp.118-126
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• 2008

Statistical image reconstruction methods have played an important role in emission computed tomography (ECT) since they accurately model the statistical noise associated with gamma-ray projection data. Although the use of statistical methods in clinical practice in early days was of a difficult problem due to high per-iteration costs and large numbers of iterations, with the development of fast algorithms and dramatically improved speed of computers, it is now inevitably becoming more practical. Some statistical methods are indeed commonly available from nuclear medicine equipment suppliers. In this paper, we first describe a mathematical background for statistical reconstruction methods, which includes assumptions underlying the Poisson statistical model, maximum likelihood and maximum a posteriori approaches, and prior models in the context of a Bayesian framework. We then review a recent progress in developing fast iterative algorithms.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.4
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• pp.713-718
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• 1987

In this paper, an iterative restoration algorithm from power spectral density with 1 bit sign information of real part of two dimensional Fourier transform of image corrupted by additive white Gaussian noise is proposed. This method is a modified version of image reconstruction algorithm from power spectral density. From the results of computer simulation with original 32 gray level imgae of 64x64 pixels, we can find that restorated image after each iteration converge to original image very fast, and SNR gain be at least 8[dB] after 10th iteration for corrupted image with additive white Gaussian noise.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.2
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• pp.1-7
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• 1984

An algorithm, which can obtain a reconstructed image from incomplete projections in computed tomography, is proposed. The algorithm is accomplished with a simple operations of iterative correction in reconstruction - reprojection process using the measured incomplete projections the object's crossection boundary, and so on, To demonstrate effectiveness of the algotithm the results of a computer simulation is presented.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05a
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• pp.282-287
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• 1996

We. develop in this study a wavelet transform method to apply to the flux reconstruction problem in reactor analysis. When we reconstruct pinwise heterogeneous flux by iterative methods, a difficulty arises due to the near singularity of the matrix as the mesh size becomes finer. Here we suggest a wavelet transform to tower the spectral radius of the near singular matrix and thus to converge by a standard iterative scheme. We find that the spectral radios becomes smatter than one after the wavelet transform is performed on sample problems.