Hyenkyun Woo

Linearized proximal alternating minimization algorithm for motion deblurring by nonlocal regularization

Abstract Non-blind motion deblurring problems are highly ill-posed and so it is quite difficult to find the original sharp and clean image. To handle ill-posedness of the motion deblurring problem, we use nonlocal total variation (abbreviated as TV) regularization approaches. Nonlocal TV can restore periodic textures and local geometric information better than local TV. But, since nonlocal TV requires weighted difference between pixels in the whole image, it demands much more computational resources than local TV. By using the linearization of the fidelity term and the proximal function, our proposed algorithm does not require any inversion of blurring operator and nonlocal operator. Therefore, the proposed algorithm is very efficient for motion deblurring problems. We compare the numerical performance of our proposed algorithm with that of several state-of-the-art algorithms for deblurring problems. Our numerical results show that the proposed method is faster and more robust than state-of-the-art algorithms on motion deblurring problems.

Proximal Linearized Alternating Direction Method for Multiplicative Denoising

Multiplicative noise naturally appears in coherent imaging systems, such as synthetic aperture radar. Due to the edge preserving feature of total variation (TV), variational models with TV regularization show good performance for multiplicative denoising. Recently proposed augmented Lagrangian frameworks with the Gauss--Seidel method are efficient for solving multiplicative denoising variational models with TV. But recent algorithms based on the augmented Lagrangian require inner iterations or an inverse involving the Laplacian operator at each iteration. In this paper, we propose efficient algorithms for TV regularized convex variational models. Our proposed algorithms do not require any inner iteration or the inverse involving the Laplacian operator by using a linearization scheme and a proximal function. We establish global convergence under a Lipschitz continuous assumption on the gradient of the fidelity term when a correction step is used. Numerical results show that our proposed algorithms overall ...

Environmentally Robust Motion Detection for Video Surveillance

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.

Two-Level Convex Relaxed Variational Model for Multiplicative Denoising

The fully developed speckle (multiplicative noise) naturally appears in coherent imaging systems, such as synthetic aperture radar. Since the speckle is multiplicative, it is difficult to interpret observed data. Total variation (TV) based variational models have recently been used in the removal of the speckle because of the strong edge preserving property of TV and reasonable computational cost. However, the fidelity term (or negative log-likelihood) of the original variational model [G. Aubert and J.-F. Aujol, SIAM J. Appl. Math., 68 (2008), pp. 925--946], which appears on maximum a posteriori (MAP) estimation, is not convex. Recently, the logarithmic transformation and the

A direct tracking method for a grounded conductor inside a pipeline from capacitance measurements

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Electrical capacitance tomography for visualizing the flow of a mixture of high/low conducting components

th root transformation have been proposed to relax the nonconvexity. It is empirically observed that the

Global Illumination Invariant Object Detection With Level Set Based Bimodal Segmentation

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Alternating minimization algorithm for shifted speckle reduction variational model

th root transform based variational model outperforms the log transform based variational model. However, the performance of the

Real-time motion detection in video surveillance using a level set-based energy functional

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Speckle Reduction Variational Model with Total Variation Regularization

th root transform based model critically depends on the choice of