• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.1
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• pp.105-114
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• 1997

The phase retrieval problem is concerned with the reconstruction of a signal or its fourier transform phase form the fourier transform magnitude of the signal. This problem does not have a unique solution, in general. If, however, the desired signal is minimum-phase, then it can be decided uniquely. This paper shows that we can make a minimum-phase signal by adding a delta function having a large value at the origin of an arbitrary one-dimensional signal, and a two-dimensional signal can be uniquely specified from its fourier transform magnitude if it is added by a delta function having a large value at the origin, and finally we can solve a two-dimensional phase retrieval problem by decomposing it into several ine-dimensional phase retrieval problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4355-4371
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• 2020

Phase retrieval, recovering a signal from phaseless measurements, is generally considered to be an NP-hard problem. This paper adopts an amplitude-based nonconvex optimization cost function to develop a new stochastic gradient algorithm, named stochastic reweighted phase retrieval (SRPR). SRPR is a stochastic gradient iteration algorithm, which runs in two stages: First, we use a truncated sample stochastic variance reduction algorithm to initialize the objective function. The second stage is the gradient refinement stage, which uses continuous updating of the amplitude-based stochastic weighted gradient algorithm to improve the initial estimate. Because of the stochastic method, each iteration of the two stages of SRPR involves only one equation. Therefore, SRPR is simple, scalable, and fast. Compared with the state-of-the-art phase retrieval algorithm, simulation results show that SRPR has a faster convergence speed and fewer magnitude-only measurements required to reconstruct the signal, under the real- or complex- cases.

• Journal of the Optical Society of Korea
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• v.14 no.2
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• pp.131-136
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• 2010

Modification of the phase gradient formulation is proposed in order to make phase retrieval less susceptible to noise. The modified formulation is derived from separation of the phase terms and the intensity modulation terms of interferograms, and subsequent differentiation to reduce the noise-induced error of the phase gradient vector. Its performance is evaluated and compared to that of the conventional formulation, and noise-robust nature is confirmed.

• Journal of the Optical Society of Korea
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• v.12 no.3
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• pp.186-191
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• 2008

A phase retrieval method using an error reduction algorithm is developed for reconstructing a wavefront aberration of an 100-TW Ti:sapphire laser pulse from the measurement of a focal spot. The phase retrieval method can successfully reconstruct a wavefront aberration of a 100-TW Ti:sapphire laser pulse, and the reconstructed wavefront aberration shows a good agreement with the wavefront aberration measured with a wavefront sensor. The effect of the dynamic range and the intensity noise on the reconstruction is also investigated in reconstructing a wavefront aberration of an 100-TW Ti:sapphire laser pulse.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2010.05a
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• pp.37-41
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• 2010

The previous investigations of electroencephalogram (EEG) activity in the memory retrieval tasks demonstrated that event-related potentials (ERP) during recollection showed different durations and the peak levels from those without recollection. However, it has been unknown that recollection in memory retrieval also modulates high-frequency brain rhythms as well as establishes large-scale synchronization across different cortical areas. In this study, we examined the spectral components of the EEG signals, especially the gamma bands (20-80Hz), measured during the memory retrieval tasks. Specifically, we focused on two major spectral components: first, we evaluated the temporal patterns of the power spectral density before and after the onset of the memory retrieval task; second, we estimated phase synchrony between all possible pairs of EEG channels to evaluate large-scale synchronization. Fourteen healthy subjects performed the memory retrieval task in the virtual reality environment where they selected whether or not t he present item was seen in the previous training period. When the subjects viewed the unseen items, the middle gamma power (40-60Hz) appeared to increase 200-500ms after stimulus onset while the low gamma power (20Hz) was suppressed all the way through the post-stimulus period 150ms after onset. The degree of phase synchronization in this low gamma level, however, increased when the subjects fetched the item from memory. This suggests that phase synchrony analysis might reveal different aspects of the memory retrieval process than the gamma power, providing additional information to the inference on the brain dynamics during memory retrieval.

• Current Optics and Photonics
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• v.6 no.5
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• pp.473-478
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• 2022

To overcome the capture-range problem in phase-diversity phase retrieval (PDPR), a geometrical-optics initial-estimate method is proposed to avoid a local minimum and to improve the accuracy of laser-wavefront measurement. We calculate the low-order aberrations through the geometrical-optics model, which is based on the two spot images in the propagation path of the laser, and provide it as a starting guess for the PDPR algorithm. Simulations show that this improves the accuracy of wavefront recovery by 62.17% compared to other initial values, and the iteration time with our method is reduced by 28.96%. That is, this approach can solve the capture-range problem.

• 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.

• 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

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.264-272
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• 2010

Example-based super resolution is a method to restore a high resolution image from low resolution images through training and retrieval of image patches. It is not only good in its performance but also available for a single frame low-resolution image. However, its time complexity is very high because it requires lots of comparisons to retrieve image patches in restoration process. In order to improve the restoration speed, an efficient patch retrieval algorithm is essential. In this paper, we applied various high-dimensional feature retrieval methods, available for the patch retrieval, to a practical example-based super resolution system and compared their speed. As well, we propose to apply the multi-phase candidate reduction approach to the patch retrieval process, which was successfully applied in character recognition fields but not used for the super resolution. In the experiments, LSH was the fastest among conventional methods. The multi-phase candidate reduction method, proposed in this paper, was even faster than LSH: For $1024{\times}1024$ images, it was 3.12 times faster than LSH.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.3
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• pp.171-180
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• 2003

A content-based image retrieval(CBIR) system builds the image database using low-level features such as color, shape and texture and provides similar images that user wants to retrieve when the retrieval request occurs. What the user is interest in is a response time in consideration of the building time to build the index database and the response time to obtain the retrieval results from the query image. In a content-based image retrieval system, the similarity computing time comparing a query with images in database takes the most time in whole response time. In this paper, we propose the two-phase search method with the clustering technique of feature vector in order to minimize the similarity computing time. Experimental results show that this two-phase search method is 2-times faster than the conventional full-search method using original features of ail images in image database, while maintaining the same retrieval relevance as the conventional full-search method. And the proposed method is more effective as the number of images increases.