Unified Theory for Recovery of Sparse Signals in a General Transform Domain

Lee, Kiryung,Li, Yanjun,Jin, Kyong Hwan,Ye, Jong Chul IEEE 2018 IEEE transactions on information theory Vol.64 No.8

<P>Compressed sensing is provided a data-acquisition paradigm for sparse signals. Remarkably, it has been shown that the practical algorithms provide robust recovery from noisy linear measurements acquired at a near optimal sampling rate. In many real-world applications, a signal of interest is typically sparse not in the canonical basis but in a certain transform domain, such as wavelets or the finite difference. The theory of compressed sensing was extended to the analysis sparsity model, but known extensions are limited to the specific choices of sensing matrix and sparsifying transform. In this paper, we propose a unified theory for robust recovery of sparse signals in a general transform domain by convex programming. In particular, our results apply to the general acquisition and sparsity models and show how the number of measurements for recovery depends on properties of measurement and sparsifying transforms. Moreover, we also provide extensions of our results to the scenarios where the atoms in the transform have varying incoherence parameters and the unknown signal exhibits a structured sparsity pattern. In particular, for the partial Fourier recovery of sparse signals over a circulant transform, our main results suggest a uniformly random sampling. Numerical results demonstrate that the variable density random sampling by our main results provides a superior recovery performance over the known sampling strategies.</P>

Regression-based prediction for blocking artifact reduction in JPEG-compressed images

Lee, Kiryung,Kim, Dong Sik,Kim, Taejeong IEEE 2005 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.14 No.1

In order to reduce the blocking artifact in the Joint Photographic Experts Group (JPEG)-compressed images, a new noniterative postprocessing algorithm is proposed. The algorithm consists of a two-step operation: low-pass filtering and then predicting. Predicting the original image from the low-pass filtered image is performed by using the predictors, which are constructed based on a broken line regression model. The constructed predictor is a generalized version of the projector onto the quantization constraint set , , or the narrow quantization constraint set . We employed different predictors depending on the frequency components in the discrete cosine transform (DCT) domain since each component has different statistical properties. Further, by using a simple classifier, we adaptively applied the predictors depending on the local variance of the DCT block. This adaptation enables an appropriate blurring depending on the smooth or detail region, and shows improved performance in terms of the average distortion and the perceptual view. For the major-edge DCT blocks, which usually suffer from the ringing artifact, the quality of fit to the regression model is usually not good. By making a modification of the regression model for such DCT blocks, we can also obtain a good perceptual view. The proposed algorithm does not employ any sophisticated edge-oriented classifiers and nonlinear filters. Compared to the previously proposed algorithms, the proposed algorithm provides comparable or better results with less computational complexity.

Compressed Sensing of Low-Rank Matrices : A Brief Survey on Efficient Algorithms

이기륭(Kiryung Lee),예종철(Jong Chul Ye) 大韓電子工學會 2009 電子工學會論文誌-SP (Signal processing) Vol.46 No.5

Compressed sensing은 소수의 선형 관측으로부터 sparse 신호를 복원하는 문제를 언급하고 있다. 최근 벡터 경우에서의 성공적인 연구 결과가 행렬의 경우로 확장되었다. Low-rank 행렬의 compressed sensing은 ill-posed inverse problem을 low-rank 정보를 이용하여 해결한다. 본 문제는 rank 최소화 혹은 low-rank 근사의 형태로 나타내질 수 있다. 본 논문에서는 최근 제안된 여러 가지 효율적인 알고리즘에 대한 survey를 제공한다. Compressed sensing addresses the recovery of a sparse vector from its few linear measurements. Recently, the success for the vector case has been extended to the matrix case. Compressed sensing of low-rank matrices solves the ill-posed inverse problem with the low-rank prior. The problem can be formulated as either the rank minimization or the low-rank approximation. In this paper, we survey recently proposed efficient algorithms to solve these two formulations.

Modified band alignment effect in ZnO/Cu₂O heterojunction solar cells via Cs₂O buffer insertion

Eom, Kiryung,Lee, Dongyoon,Kim, Seunghwan,Seo, Hyungtak IOP 2018 Journal of Physics. D, Applied Physics Vol.51 No.5

<P>The effects of a complex buffer layer of cesium oxide (Cs<SUB>2</SUB>O) on the photocurrent response in oxide heterojunction solar cells (HSCs) were investigated. A <I>p</I>-<I>n</I> junction oxide HSC was fabricated using <I>p</I>-type copper (I) oxide (Cu<SUB>2</SUB>O) and <I>n</I>-type zinc oxide (ZnO); the buffer layer was inserted between the Cu<SUB>2</SUB>O and fluorine-doped tin oxide (FTO). Ultraviolet–visible (UV–vis) and x-ray and ultraviolet photoelectron spectroscopy analyses were performed to characterize the electronic band structures of cells, both with and without this buffer layer. In conjunction with the measured band electronic structures, the significantly improved visible-range photocurrent spectra of the buffer-inserted HSC were analyzed in-depth. As a result, the 1 sun power conversion efficiency was increased by about three times by the insertion of buffer layer. The physicochemical origin of the photocurrent enhancement was mainly ascribed to the increased photocarrier density in the buffer layer and modified valence band offset to promote the effective hole transfer at the interface to FTO on the band-alignment model.</P>

Block-Coordinate Gauss–Newton Optimization and Constrained Monotone Regression for Image Registration in the Presence of Outlier Objects

Dong Sik Kim,Kiryung Lee IEEE 2008 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.17 No.5

<P>In this paper, we propose the block-coordinate Gauss-Newton/regression method in order to conduct a correlation-based registration considering the intensity difference between images in the presence of outlier objects. In the proposed method, the parameters are decomposed into two blocks, one of which is for the spatial registration and the other for the intensity compensation. The two blocks are sequentially updated by the Gauss-Newton update and the polynomial regression, respectively. Because of the separated blocks, we can perform a joint optimization with low computational complexity and high implementation flexibility. For example, we apply separately appropriate scaling techniques to the parameter blocks for a stable and fast convergence of the algorithm. Furthermore, we apply the constrained monotone regression with a robust outlier detection scheme for the intensity compensation block. From numerical results, it is shown that the proposed algorithm more effectively performs a correlation-based registration considering the intensity difference alleviating the influence of the outlier objects compared to the traditional registration algorithms that perform the joint optimization.</P>

1995년 서울지역 거주자에서 발생한 Plasmodium Vivax에 의한 Malaria 1예

김영선,박기령,어은경,최진혁,이정수,정화순 梨花女子大學校 醫科大學 醫科學硏究所 1996 EMJ (Ewha medical journal) Vol.19 No.2

Malaria has a wordwide incidence of more than one thousand to three thousand million clinical cases and results in approximately 200 to 300 million deaths per year. Although Korea was an endemic area of Malaria, it was nearly eradicated from the country. However, indigenous cases of malaria have been reported since 1994. Recently, we experienced a case of Plasmodium vivax infection in a resident of Seoul. A 25-year old young man who had no history of being abroad, blood transfusion nor parenteral use of drug visited our hospital due to fever and chill for 4 days. A peripheral blood smear demonstrated Plasmodium vivax infection. He was treated with hydroxychoroquine and premaquine without relapse.

Colorimetric Effect of Au Nanoparticle Chain/Polymer Film under Mechanical Stress and Gas Pressure

Shim, Gowoon,Eom, Kiryung,Lee, Gyuyeon,Seo, Hyungtak Materials Research Society of Korea 2018 한국재료학회지 Vol.28 No.1

Gas detection is necessary for various reasons, including the prevention of gas leakages and the creation of necessary environmental conditions. Among the gas detection methods, leakage of gas can be confirmed using materials that undergo color changes that are easily distinguished by the naked eye. Metal nanoparticles (NPs) experience variations in their absorption wavelengths under the localized surface plasmon effect (LSPR) with mechanical stresses, which change the distance between NPs. In this study, we attempted to detect the presence of gas utilizing the LSPR-related color change of a chain of Au NPs. The assembly of Au NPs, arranged in a chain shape, experienced a color change from dark blue to purple with a change in the distance between the NPs by applying a physical force, i.e., compression, stretching, and gas pressure. As the force of compression and the degree of stretching increased, the absorption wavelength shifted from doublet peaks at 650 and 550 nm to a singlet peak at 550 nm. Further, applying gas pressure caused an identical color change. With this result, we propose a method that could be applied to all gases that require detection based on gas pressure.

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

Ye, Jong Chul,Kim, Jong Min,Jin, Kyong Hwan,Lee, Kiryung IEEE 2017 IEEE transactions on information theory Vol.63 No.2

<P>While the recent theory of compressed sensing provides an opportunity to overcome the Nyquist limit in recovering sparse signals, a solution approach usually takes the form of an inverse problem of an unknown signal, which is crucially dependent on specific signal representation. In this paper, we propose a drastically different two-step Fourier compressive sampling framework in a continuous domain that can be implemented via measurement domain interpolation, after which signal reconstruction can be done using classical analytic reconstruction methods. The main idea originates from the fundamental duality between the sparsity in the primary space and the low-rankness of a structured matrix in the spectral domain, showing that a low-rank interpolator in the spectral domain can enjoy all of the benefits of sparse recovery with performance guarantees. Most notably, the proposed low-rank interpolation approach can be regarded as a generalization of recent spectral compressed sensing to recover large classes of finite rate of innovations (FRI) signals at a near-optimal sampling rate. Moreover, for the case of cardinal representation, we can show that the proposed low-rank interpolation scheme will benefit from inherent regularization and an optimal incoherence parameter. Using a powerful dual certificate and the golfing scheme, we show that the new framework still achieves a near-optimal sampling rate for a general class of FRI signal recovery, while the sampling rate can be further reduced for a class of cardinal splines. Numerical results using various types of FRI signals confirm that the proposed low-rank interpolation approach offers significantly better phase transitions than conventional compressive sampling approaches.</P>