• IEIE Transactions on Smart Processing and Computing
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• v.6 no.2
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• pp.85-92
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• 2017

This paper presents a dehazing method based on a fuzzy membership function and variational method. The proposed algorithm consists of three steps: i) estimate transmission through a pixel-based operation using a fuzzy membership function, ii) refine the transmission using an L1-norm-based regularization method, and iii) obtain the result of haze removal based on a hazy image formation model using the refined transmission. In order to prevent color distortion of the sky region seen in conventional methods, we use a trapezoid-type fuzzy membership function. The proposed method acquires high-quality images without halo artifacts and loss of color contrast.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.6
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• pp.413-421
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• 2015

Regularized Blind Deconvolution is a problem applicable in degraded images in order to bring the original image out of blur. Multichannel blind Deconvolution considered as an optimization problem. Each step in the optimization is considered as variable splitting problem using an algorithm called Alternating Minimization Algorithm. Each Step in the Variable splitting undergoes Augmented Lagrangian method (ALM) / Bregman Iterative method. Regularization is used where an ill posed problem converted into a well posed problem. Two well known regularizers are Tikhonov class and Total Variation (TV) / L2 model. TV can be isotropic and anisotropic, where isotropic for L2 norm and anisotropic for L1 norm. Based on many probabilistic model and Fourier Transforms Image deblurring can be solved. Here in this paper to improve the performance, we have used an adaptive regularization filtering and isotropic TV model Lp norm. Image deblurring is applicable in the areas such as medical image sensing, astrophotography, traffic signal monitoring, remote sensors, case investigation and even images that are taken using a digital camera / mobile cameras.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.614-618
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• 2003

This paper presents a system identification (SI) scheme in time domain using measured acceleration data. The error function is defined as the time integral of the least square errors between the measured acceleration and the calculated acceleration by a mathmatical model. Damping parameters as well as stiffness properties of a structure are considered as system parameters. The structural damping is modeled by the Rayleigh damping. A new regularization function defined by the L$_1$-norm of the first derivative of system parameters with respect to time is proposed to alleviate the ill-posed characteristics of inverse problems and to accommodate discontinuities of system parameters in time. The time window concept is proposed to trace variation of system parameters in time.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2005.03a
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• pp.529-535
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• 2005

This paper presents a system identification (SI) scheme in time domain using measured acceleration data. The error function is defined as the time integral of the least squared errors between the measured acceleration and the calculated acceleration by a mathematical model. Damping parameters as well as stiffness properties of a structure are considered as system parameters. The structural damping is modeled by the Rayleigh damping. A new regularization function defined by the L1-norm of the first derivative of system parameters with respect to time is proposed to alleviate the ill-posed characteristics of inverse problems and to accommodate discontinuities of system parameters in time. The time window concept is proposed to trace variation of system parameters in time. Numerical simulation study is performed through a two-span continuous truss subject to ground motion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1569-1586
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• 2015

This paper considers the optimal cell association and resource allocation for load balancing in a heterogeneous cellular network subject to user's quality-of-service (QoS) constraints. We adopt the proportional fairness (PF) utility maximization formulation which also accommodates the QoS constraints in terms of minimum rate requirements. With equal resource allocation this joint optimization problem is either infeasible or requires relaxation that yields a solution which is difficult to implement. Nevertheless, we show that this joint optimization problem can be effectively solved without any priori assumption on resource allocation and yields a cell association scheme which enforces single BS association for each user. We re-formulated the joint optimization problem as a network-wide resource allocation problem with cardinality constraints. A reweighted heuristic l₁-norm regularization method is used to obtain a sparse solution to the re-formulated problem. The cell association scheme is then derived from the sparsity pattern of the solution, which guarantees a single BS association for each user. Compared with the previously proposed method based on equal resource allocation, the proposed framework results in a feasible cell association scheme and yields a robust solution on resource allocation that satisfies the QoS constraints. Our simulations illustrate the impact of user's minimum rate requirements on cell association and demonstrate that the proposed approach achieves load balancing and enforces single BS association for users.

• Journal of the Optical Society of Korea
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• v.20 no.6
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• pp.752-761
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• 2016

Hyperspectral images are often contaminated with stripe noise, which severely degrades the imaging quality and the precision of the subsequent processing. In this paper, a variational model is proposed by employing spectral-spatial adaptive unidirectional variation and a sparse representation. Unlike traditional methods, we exploit the spectral correction and remove stripes in different bands and different regions adaptively, instead of selecting parameters band by band. The regularization strength adapts to the spectrally varying stripe intensities and the spatially varying texture information. Spectral correlation is exploited via dictionary learning in the sparse representation framework to prevent spectral distortion. Moreover, the minimization problem, which contains two unsmooth and inseparable $l_1$-norm terms, is optimized by the split Bregman approach. Experimental results, on datasets from several imaging systems, demonstrate that the proposed method can remove stripe noise effectively and adaptively, as well as preserve original detail information.