• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1122-1128
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• 2007

The denoising for image restoration based on the Wavelet Packet Transform with AA(Absolute Average) making-threshold is presented. The wavelet packet transform leads to be better in the part of high frequency than wavelet transform to eliminate noise. And the existing threshold determination is used standard deviation estimated results in increasing the noise and threshold, and damaging an image quality. In addition that is decreased image restoration PSNR by using the same threshold in spite of changing image because of installing a threshold in proportion of noise size. In contrast the AA thresholding method with wavelet packet is adapted by changing image to set up threshold by statistic quantity of resolved image and is avoided an extreme impact. The results on the experiment has improved 10% and 5% over than the denoising based on simple wavelet transform and wavelet packet respectively.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.60-63
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• 2020

With the advent of deep learning, a lot of attempts have been made in computer vision to substitute deep learning models for conventional algorithms. Among them, image classification, object detection, and image restoration have received a lot of attention from researchers. However, most of the contributions were refined in one of the fields only. We propose a new paradigm of model structure. End-to-end model which we will introduce classifies noise of an image and restores accordingly. Through this, the model enhances universality and efficiency. Our proposed model is an 'One-For-All' model which classifies weather condition in an image and returns clean image accordingly. By separating weather conditions, restoration model became more compact as well as effective in reducing raindrops, snowflakes, or haze in an image which degrade the quality of the image.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.69-71
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• 2005

Earthquake disaster frequently happens in Shizuoka Prefecture and it is commonly predicted that a giant earthquake(Tokai Earthquake) could occur in the near future. When a giant earthquake happens, extensive damage of lifelines will be expected. It is considered that the collection of damage information and the establishment of a communication network system are very important in order to restore lifelines quickly. And geographic information system(GIS) might playa very important role to grasp the spatial information of lifeline damage in a natural disaster. The authors' group had a research project to study a lifeline restoration support system with image processing and ad hoc networking in a natural disaster. The objectives of this presentation are to introduce our project and to show some results of our study. The authors finally constructed the GIS for the integration of damage information acquired by image processing and ad hoc networking.

• Journal of the Korean Mathematical Society
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• v.55 no.3
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• pp.719-734
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• 2018

An image restoration problem with Poisson noise arises in many applications of medical imaging, astronomy, and microscopy. To overcome ill-posedness, Total Variation (TV) model is commonly used owing to edge preserving property. Since staircase artifacts are observed in restored smooth regions, higher-order TV regularization is introduced. However, sharpness of edges in the image is also attenuated. To compromise benefits of TV and higher-order TV, the weighted sum of the non-convex TV and non-convex higher order TV is used as a regularizer in the proposed variational model. The proposed model is non-convex and non-smooth, and so it is very challenging to solve the model. We propose an iterative reweighted algorithm with the proximal linearized alternating direction method of multipliers to solve the proposed model and study convergence properties of the algorithm.

• Journal of Information Technology Services
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• v.8 no.4
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• pp.103-113
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• 2009

The industry of cultural digital contents has been regarded as the main next generation industry. Recently techniques for restoring cultural assets and the multiple usage of those have been developed in our country. Using computer graphics and image processing techniques, many cultural assets have been restored. If the focus for researches and supports to cultural assets restoration grows rapidly, we will create cultural and economical added-values and get the benefit of that technology. In this paper, we introduce the techniques for restoring cultural assets focused on computer graphics and image processing techniques.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.7
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• pp.535-544
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• 1990

In this paper, we represent the restoration and feature extraction of fingerprint image. The purpose of restoration of fingerprint image are to com pensate distortion which is affected by noise and to preserve various features of fingerprint image. To extracte the central point of fingerprint, we used sample matrix, and restore fingerprint, we used direction in formation of thinned image and the gray scale of the original images.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.41-48
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• 2022

This paper presents the unified Bayesian Tikhonov regularization method as a solution to total variation regularization. The integrated method presents a formula for obtaining the regularization parameter by transforming the total variation term into a weighted Tikhonov regularization term. It repeats until the reconstructed image converges to obtain a regularization parameter and a new weighting factor based on it. The experimental results show the effectiveness of the proposed method for the image restoration problem.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.41-44
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• 2021

In this paper, we propose an efficient reference-based compression artifact reduction network for 360-degree images in an equi-rectangular projection (ERP) domain. In our insight, conventional image restoration methods cannot be applied straightforwardly to 360-degree images due to the spherical distortion. To address this problem, we propose an adaptive disparity estimator using a deformable convolution to exploit correlation among 360-degree images. With the help of the proposed convolution, the disparity estimator establishes the spatial correspondence successfully between the ERPs and extract matched textures to be used for image restoration. The experimental results demonstrate that the proposed algorithm provides reliable high-quality textures from the reference and improves the quality of the restored image as compared to the state-of-the-art single image restoration methods.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.57-62
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• 1999

Estimation of the point spread function (PSF) is one of the main research topic of image processing, because it determines the performance of the auto-focusing system. In this paper, a new algorithm for PSF estimation is proposed, and its application to image restoration is also presented. The procedure for complete realization of the auto-focusing system consists of two steps: PSF estimation based on edge classification, and image restoration using the estimated PSF. More specifically, we divide imput image into multiple small image or block, estimate unit step response and average them on the blocks which contain edge, and estimate 2-dimensional isotropic PSF from the 1 dimensional step response. Finally we obtain in-focused image by using image restoration based on the estimated PSF.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.209-212
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• 2007

Digital images are corrupted by noises, during signal acquisition and transmission. Amount those noises, additive white Gaussian noise (AWGN) and impulse noise are most representative. The conventional image restoration algorithms are mostly taken in simple noise environment, but they didn't perform very well in tempter noises environment. So a modified image restoration algorithm, which can remove complex noises by using the intensity differences and spatial distances between center pixel and its neighbor pixels as parameters, is proposed in this paper. Simulation results demonstrate that the proposed algorithm can't only remove AWGN and impulse noise separately, but also performs well in preserving details of images as edge.