• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.672-684
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• 2020

Restoring a low resolution and motion blurred light field has become essential due to the growing works on parallax-based image processing. These tasks are known as light-field enhancement process. Unfortunately, only a few state-of-the-art methods are introduced to solve the multiple problems jointly. In this work, we design a framework that jointly solves light field spatial super-resolution and motion deblurring tasks. Particularly, we generate a straight-forward neural network that is trained under low-resolution and 6-degree-of-freedom (6-DOF) motion-blurred light field dataset. Furthermore, we propose the strategy of local region optimization on the adversarial network to boost the performance. We evaluate our method through both quantitative and qualitative measurements and exhibit superior performance compared to the state-of-the-art methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.12
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• pp.130-139
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• 2014

Undesired camera shaking can make a blur effect, which causes a degradation of video quality. We propose an efficient method of removing the blur effects on video captured from a single camera. The proposed method has a sequential process that is applied to each frame. The first stage is to estimate the camera motion for each frame. In order to estimate the camera motion, we compute the optical flow using 3 consecutive frames. Then a patch-wise image deconvolution is applied. During the deconvolution, edge prediction is used to improve the quality of image deconvolution. After patch-wise image deconvolution, deblurred patches are integrated into an image to produce a deblurred frame. The above process is performed for each frame. The experimental result shows that the proposed method removes the blur effect efficiently.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5129-5152
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• 2016

Multi-view super-resolution (MVSR) aims to estimate a high-resolution (HR) image from a set of low-resolution (LR) images that are captured from different viewpoints (typically by different cameras). MVSR is usually applied in camera array imaging. Given that MVSR is an ill-posed problem and is typically computationally costly, we super-resolve multi-view LR images of the original scene via image fusion (IF) and blind deblurring (BD). First, we reformulate the MVSR problem into two easier problems: an IF problem and a BD problem. We further solve the IF problem on the premise of calculating the depth map of the desired image ahead, and then solve the BD problem, in which the optimization problems with respect to the desired image and with respect to the unknown blur are efficiently addressed by the alternating direction method of multipliers (ADMM). Our approach bridges the gap between MVSR and BD, taking advantages of existing BD methods to address MVSR. Thus, this approach is appropriate for camera array imaging because the blur kernel is typically unknown in practice. Corresponding experimental results using real and synthetic images demonstrate the effectiveness of the proposed method.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.409-419
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• 2009

In this article, an explicit solution of the EM algorithm for the image deburring is presented. To obtain the restore image from the strictly iterative EM algorithm is quite time-consumed and impractical in particular when the underlying observed image is not small and the number of iterations required to converge is large. The explicit solution provides a quite reasonable restore image although it exploits the approximation in the outside of the valid area of image, and also allows to obtain the effective EM solutions without iteration process in real-time in practice by using the discrete finite Fourier transformation.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.414-417
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• 2007

In this paper we present an adaptive method for accelerating conventional Maximum Entropy Method (MEM) for restoration of Passive Millimeter-Wave (PMMW) image from its blurred and noisy version. MEM is nonlinear and its convergence is very slow. We present a new method to accelerate the MEM by using an exponent on the correction ratio. In this method the exponent is computed adaptively in each iteration, using first-order derivatives of deblurred image in previous two iterations. Using this exponent the accelerated MEM emphasizes speed at the beginning stages and stability at later stages. In accelerated MEM the non-negativity is automatically ensured and also conservation of flux without additional computation. Simulation study shows that the accelerated MEM gives better results in terms of RMSE, SNR, moreover, it takes only about 46% lesser iterations than conventional MEM. This is also confirmed by applying this algorithm on actual PMMW image captured by 94 GHz mechanically scanned radiometer.

• 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 KIEE Conference
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• 1979.08a
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• pp.155-157
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• 1979

The Fourier convolution algorithms are used to reconstruct a 3-D density function from the projection data sets. The convolved data are then back projected to obtain a density function. There are several choices of the weighting function for the design of the reconstruction(deblurring) filter. Present paper reviews the design of reconstruction filter considering the problems such as the effects of sampling rate, aliasing, and noise.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.289-291
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• 2005

In this paper, we introduce the backward solution of nonlinear wave equation for denoising. The PDE method is approved about 4 PSNR value compare with any convolution method. In neuro images, denoising process using proposed PDE is good about 0.2% increased Voxel Region.

• Journal of the Chungcheong Mathematical Society
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• v.30 no.1
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• pp.117-128
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• 2017

As an efficient way to determine the regularization parameter in the discrete ill-posed problems with multiple right-hand sides, we suggest global L-curve criterion as an extension of L-curve technique for image restoration problems with single right-hand side.

• Journal of the Chungcheong Mathematical Society
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• v.32 no.1
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• pp.149-156
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• 2019

This paper present the global generalized cross validation as the appropriate choice of the regularization parameter in the preconditioned Gl-LSQR method in solving image deblurring problems. The regularization parameter, chosen from the global generalized cross validation, with preconditioned Gl-LSQR method can give better reconstructions of the true image than other parameters considered in this study.