• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2748-2766
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• 2016

Compared with traditional patch-based sparse representation, recent studies have concluded that group-based sparse representation (GSR) can simultaneously enforce the intrinsic local sparsity and nonlocal self-similarity of images within a unified framework. This article investigates an accelerated split Bregman method (SBM) that is based on GSR which exploits image compressive sensing (CS). The computational efficiency of accelerated SBM for the measurement matrix of a partial Fourier matrix can be further improved by the introduction of a fast Fourier transform (FFT) to derive the enhanced algorithm. In addition, we provide convergence analysis for the proposed method. Experimental results demonstrate that accelerated SBM is potentially faster than some existing image CS reconstruction methods.

• 한국통신학회논문지
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• 제40권6호
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• pp.1024-1031
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• 2015

압축 센싱은 신호가 성긴 (Sparse) 특성을 지니며 선형 측정된 값들이 Incoherent 할 때, 나이퀴스트율 이하로 표본화된 신호를 원본 신호로 정확하게 복구할 수 있는 새로운 신호 획득 이론이다. 본 논문에서는 원본 신호의 Sparse한 정도에 따라 성능이 변화하는 압축 센싱을 이용한 효율적인 신호 검출 및 추정 기법을 제안하며, 이론적 분석과 함께 모의 실험 결과를 보여준다.

• 대한기계학회논문집B
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• 제30권1호
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• pp.8-15
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• 2006

A Sparse Point Representation(SPR) based on interpolation wavelets is presented. The SPR is implemented for the purpose of CFD data compression. Unlike conventional wavelet transformation, the SPR relieves computing workload in the similar fashion of lifting scheme that includes splitting and prediction procedures in sequence. However, SPR skips update procedure that is major part of lifting scheme. Data compression can be achieved by proper thresholding method. The advantage of the SPR method is that, by keeping even point physical values, low frequency filtering procedure is omitted and its related unphysical thresholing mechanism can be avoided in reconstruction process. Extra singular feature detection algorithm is implemented for preserving singular features such as shock and vortices. Several numerical tests show the adequacy of SPR for the CFD data. It is also shown that it can be easily extended to nonlinear adaptive wavelets for enhanced feature capturing.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2015년도 추계학술대회
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• pp.1-3
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• 2015

In this paper, we propose a sparse signal reconstruction method referred to as the matching pursuit with a pruning-based tree search (PTS-MP). Two key ingredients of PTS-MP are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree. In our simulations, we confirm that PTS-MP is effective in recovering sparse signals and outperforms conventional sparse recovery algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1464-1480
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• 2019

Robust tracking of infrared (IR) pedestrian targets with various backgrounds, e.g. appearance changes, illumination variations, and background disturbances, is a great challenge in the infrared image processing field. In the paper, we address a new tracking method for IR pedestrian targets via multi-feature local sparse representation (SR), which consists of three important modules. In the first module, a multi-feature local SR model is constructed. Considering the characterization of infrared pedestrian targets, the gray and edge features are first extracted from all target templates, and then fused into the model learning process. In the second module, an effective tracker is proposed via the learned model. To improve the computational efficiency, a sliding window mechanism with multiple scales is first used to scan the current frame to sample the target candidates. Then, the candidates are recognized via sparse reconstruction residual analysis. In the third module, an adaptive dictionary update approach is designed to further improve the tracking performance. The results demonstrate that our method outperforms several classical methods for infrared pedestrian tracking.

• Current Optics and Photonics
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• 제5권5호
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• pp.562-575
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• 2021

As a new type of spectrometer, that based on filters with different transmittance features attracts a lot of attention for its advantages such as small-size, low cost, and simple optical structure. It uses post-processing algorithms to achieve target spectrum reconstruction; therefore, the performance of the spectrometer is severely affected by noise. The influence of noise on the spectral reconstruction results is studied in this paper, and suggestions for solving the spectral reconstruction problem under noisy conditions are given. We first list different spectral reconstruction methods, and through simulations demonstrate that these methods show unsatisfactory performance under noisy conditions. Then we propose to apply the gradient projection for sparse reconstruction (GRSR) algorithm to the spectral reconstruction method. Simulation results show that the proposed method can significantly reduce the influence of noise on the spectral reconstruction process. Meanwhile, the accuracy of the spectral reconstruction results is dramatically improved. Therefore, the practicality of the filter-based spectrometer will be enhanced.

• 한국통신학회논문지
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• 제39A권12호
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• pp.756-763
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• 2014

본 논문에서는 트리검색 기반의 GTMP (matching pursuit with greedy tree search)이라는 새로운 희소신호 복원기법을 제안한다. 트리검색은 비용함수 (cost function)를 최소화함으로써 희소신호 복원 성능을 향상시키기 위해 적용하였다. 또한 각 노드마다 트리제거 (tree pruning)기법을 이용하여 효율적인 알고리듬을 개발하였다. 본 논문에서는 알고리듬의 성능분석을 통해 희소신호에서 영(0)이 아닌 값의 위치를 정확히 찾아내는 조건을 도출하였다. 그리고 실험을 통해 GTMP가 기존의 희소신호 복원기법에 비해 성능이 향상되었음을 보였다.

• 대한전자공학회논문지SP
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• 제48권2호
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• pp.134-140
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• 2011

압축 센싱 (Compressed Sensing) 기술을 통해 $M{\times}N$ 측정 행렬의 원소들이 특정의 독립적인 확률 분포에서 뽑혀 identically 분포의 성질을 가지고 있을 때 $M{\ll}N$의 경우에도 스파스 (sparse) 신호를 높은 확률로 정확하게 복원할 수 있다. $L_1$-최소화 알고리즘이 불완전한 측정에 대해서도 스파스 (sparse) 신호를 복원할 수 있다는 것은 잘 알려진 사실이다. 본 논문에서는 OMP를 변형시킨 support 검출과 가중치 기법을 이용한 $L_1$-최소화 방법을 통하여 스파스 (sparse) 신호의 복원 성능을 향상시키는 알고리즘을 제안하고자 한다.

• 한국전자파학회논문지
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• 제25권4호
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• pp.467-475
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• 2014

본 논문에서는 1차원 레이더 특성(signature)인 고해상도 거리 측면도(HRRP)와 2차원 레이더 특성인 ISAR 영상을 형성하기 위하여 CS(Compressive Sensing) 기반의 레이더 신호 모델을 적용한 sparse 복원(sparse recovery) 알고리즘을 소개하고자 한다. 만약, 관측된 RCS(Radar Cross Section) 데이터 샘플에서 데이터 손실이 발생할 경우, 기존의 discrete Fourier transform(DFT) 방식으로는 올바른 고해상도의 레이더 특성들을 얻을 수 없다. 하지만, 데이터 손실이 존재하더라도 상기 sparse 복원 알고리즘을 적용하면 고해상도의 레이더 특성을 성공적으로 복원할 수 있고, 원래 광대역의 RCS 데이터를 이용한 레이더 특성과 동등하게 고해상도를 유지할 수 있다. 따라서, 본 논문에서 보여준 결과에서와 같이 원하지 않는 간섭신호나 전파 교란 신호에 의해 데이터 손실이 발생한 RCS 데이터를 수집하더라도, sparse 복원 알고리즘을 이용하면 기존 DFT 방식과 달리 고해상도의 레이더 특성을 성공적으로 복원할 수 있음을 관찰할 수 있었다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.103-107
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• 2009

This paper presents how to minimize the second-order cone programming problem occurring in the 3D reconstruction of multiple views. The $L_{\infty}$-norm minimization is done by a series of the minimization of the maximum infeasibility. Since the problem has many inequality constraints, we have to adopt methods of the interior point algorithm, in which the inequalities are sequentially approximated by log-barrier functions. An initial feasible solution is found easily by the construction of the problem. Actual computing is done by an iterative Newton-style update. When we apply the interior point method to the problem of reconstructing the structure and motion, every Newton update requires to solve a very large system of linear equations. We show that the sparse bundle-adjustment technique can be utilized in the same way during the Newton update, and therefore we obtain a very efficient computation.