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RISS 인기검색어
- 검색키워드
- 저자 : Hyenkyun Woo (검색결과 10 건)
- 무료
- 기관 내 무료
- 유료
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FAST NONLOCAL REGULARIZATION METHOD FOR IMAGE RESTORATION
Hyenkyun Woo,Sangwoon Yun 한국산업응용수학회 2010 한국산업응용수학회 학술대회 논문집 Vol.5 No.1
In this paper, we propose an alternating minimization algorithm to solve nonlocal total variation based minimization problems. Because nonlocal total variation are designed based on self-similarity of images, it is very useful for various image restoration problems. Recently, several efficient optimization methods are developed to solve the Nonlocal TV minimization problem[4,6]. These methods are efficient but slow to handle deblurring problem. In this paper, we show how to efficiently enhance blurred image with alternating minimization algorithm[7] and Bregman operator splitting method[6].
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Environmentally Robust Motion Detection for Video Surveillance
Hyenkyun Woo,Yoon Mo Jung,Jeong-Gyoo Kim,Jin Keun Seo IEEE 2010 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.19 No.11
<P>Most video surveillance systems require to manually set a motion detection sensitivity level to generate motion alarms. The performance of motion detection algorithms, embedded in closed circuit television (CCTV) camera and digital video recorder (DVR), usually depends upon the preselected motion sensitivity level, which is expected to work in all environmental conditions. Due to the preselected sensitivity level, false alarms and detection failures usually exist in video surveillance systems. The proposed motion detection model based upon variational energy provides a robust detection method at various illumination changes and noise levels of image sequences without tuning any parameter manually. We analyze the structure mathematically and demonstrate the effectiveness of the proposed model with numerous experiments in various environmental conditions. Due to the compact structure and efficiency of the proposed model, it could be implemented in a small embedded system.</P>
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Alternating Minimization Algorithm for Speckle Reduction With a Shifting Technique
Hyenkyun Woo,Sangwoon Yun IEEE 2012 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.21 No.4
<P>Speckles (multiplicative noise) in synthetic aperture radar (SAR) make it difficult to interpret the observed image. Due to the edge-preserving feature of total variation (TV), variational models with TV regularization have attracted much interest in reducing speckles. Algorithms based on the augmented Lagrangian function have been proposed to efficiently solve speckle-reduction variational models with TV regularization. However, these algorithms require inner iterations or inverses involving the Laplacian operator at each iteration. In this paper, we adapt Tseng's alternating minimization algorithm with a shifting technique to efficiently remove the speckle without any inner iterations or inverses involving the Laplacian operator. The proposed method is very simple and highly parallelizable; therefore, it is very efficient to despeckle huge-size SAR images. Numerical results show that our proposed method outperforms the state-of-the-art algorithms for speckle-reduction variational models with a TV regularizer in terms of central-processing-unit time.</P>
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Real-time motion detection in video surveillance using a level set-based energy functional
Hyenkyun Woo,Min Ok Lee,Jin Keun Seo 한국산업응용수학회 2007 한국산업응용수학회 학술대회 논문집 Vol.2 No.1
This paper presents a real-time motion detection algorithm for surveillance application based on a new level set-based energy functional. The proposed algorithm for minimizing the energy functional combines automatically motion segmentation and denoising operation in real time, and it provides robust and efficient motion detection at various noise levels of image sequences. Experimental results using surveillance camera show that it is very efficient in segmenting moving objects regardless of environment conditions. It has a very low false alarm rate even at night, when relatively few motion occur.
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A New Multiplicative Denoising Variational Model Based on <tex> $m$</tex>th Root Transformation
Sangwoon Yun,Hyenkyun Woo IEEE 2012 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.21 No.5
<P>In coherent imaging systems, such as the synthetic aperture radar (SAR), the observed images are contaminated by multiplicative noise. Due to the edge-preserving feature of the total variation (TV), variational models with TV regularization have attracted much interest in removing multiplicative noise. However, the fidelity term of the variational model, based on maximum a posteriori estimation, is not convex, and so, it is usually difficult to find a global solution. Hence, the logarithmic function is used to transform the nonconvex variational model to the convex one. In this paper, instead of using the log, we exploit the th root function to relax the nonconvexity of the variational model. An algorithm based on the augmented Lagrangian function, which has been applied to solve the log transformed convex variational model, can be applied to solve our proposed model. However, this algorithm requires solving a subproblem, which does not have a closed-form solution, at each iteration. Hence, we propose to adapt the linearized proximal alternating minimization algorithm, which does not require inner iterations for solving the subproblems. In addition, the proposed method is very simple and highly parallelizable; thus, it is efficient to remove multiplicative noise in huge SAR images. The proposed model for multiplicative noise removal shows overall better performance than the convex model based on the log transformation.</P>
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Global Illumination Invariant Object Detection With Level Set Based Bimodal Segmentation
Suk-Ho Lee,Hyenkyun Woo,Moon Gi kang IEEE 2010 IEEE transactions on circuits and systems for vide Vol.20 No.4
<P>In this letter, we propose a new detection method for video surveillance which provides for a robust and real-time working object detection under various global illumination conditions. The proposed scheme needs no manual parameter settings for different illumination conditions, which makes the algorithm applicable to automatic surveillance systems. Two special filters are designed to eliminate the spurious object regions that occur due to the charge coupled device (CCD) noise, making the scheme stable even in very low illumination conditions. We demonstrate the effectiveness of the proposed algorithm experimentally with different illumination conditions, changes in contrast, and noise level.</P>
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