• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1903-1906
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• 2003

This paper proposes an adaptive regularized noise smoothing algorithm for range image using the area decreasing flow method, which can preserve meaningful edges during the smoothing process. Although the area decreasing flow method can easily smooth Gaussian noise, it has two problems; ⅰ) it is not easy to remove impulsive noise from observed range data, and ⅱ) it is also difficult to remove noise near edge when the adaptive regularization is used. In the paper, therefore, the second smoothness constraint is addtionally incorporated into the existing regularization algorithm, which minimizes the difference between the median filtered data and the estimated data. As a result, the Proposed algorithm can effectively remove the noise of dense range data with edge preserving.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2173-2179
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• 2015

In digital era image processing has been utilized in a variety of media such as TV, camera and smart phone. Typically salt and pepper noise are generated by various causes during the analysis, identification, and processing of image data. Principal filters such as SMF, CWMF, and AMF have been used to remove these noise. But the existing filters fall short of edge preservation and noise elimination in high noise densities. Thus, a processing algorithm, on which the size of deformable mask varies depending on the noise density, is proposed to remove salt and pepper noise effectively in this study. The performance of the proposed method was evaluated compared with the existing methods using PSNR.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.675-681
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• 2019

Noise removal is a pre-requisite procedure in image processing, and various methods have been studied depending on the type of noise and the environment of the image. However, for image processing with high-frequency components, conventional additive white Gaussian noise (AWGN) removal techniques are rather lacking in performance because of the blurring phenomenon induced thereby. In this paper, we propose an algorithm to minimize the blurring in AWGN removal processes. The proposed algorithm sets the high-frequency and the low-frequency component filters, respectively, depending on the pixel properties in the mask, consequently calculating the output of each filter with the addition or subtraction of the input image to the reference. The final output image is obtained by adding the weighted data calculated using the standard deviations and the Gaussian distribution with the output of the two filters. The proposed algorithm shows improved AWGN removal performance compared to the existing method, which was verified by simulation.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.5
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• pp.347-352
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• 2013

In impulse noise filtering techniques window size play an important role. Usually, an appropriate window is determined according to the noise density. A small window may not be able to suppress noise properly whereas a large window may remove edges and fine image details. Moreover, the value of the central pixel is estimated by considering all pixels within the window. In this work, contrary to the previous approaches, we propose an iterative impulse noise removal scheme that emphasizes on noise-free pixels within a small neighborhood. The iterative process continues until all noisy pixels are replaced with the estimated pixels. In order to estimate the optimal value for a noisy pixel, a genetic programming (GP) based estimator is evolved that takes few noise-free pixels as input. The estimator is constituent of noise-free pixels, arithmetic operators and random constants. Experimental results show that theproposed scheme is capable of removing impulse noise effectively while preserving the fine image details. Especially, our approach has shown effectiveness against high impulse noise density.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.9
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• pp.789-796
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• 2013

Vibration measurements using image processing have been studied by many researchers as it can remotely measure vibration displacements at multiple points simultaneously. It is difficult, however, to obtain accurate displacement from the measured image signals because the resolution of image data is dependent on camera performance and normally lower than that of vibration transducer directly measured. This paper suggests the enhanced technique for vibration displacement measurement by applying the expected value of edge probability distribution to the varying pixel points in the image. The method can both increase the resolution limit of camera image and decrease the measurement errors. The working performance of the proposed technique is verified applying to the vibration measurement of a rotating machine.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.503-507
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• 2004

This paper is concerned with the measurement of low-frequency vibrations of structures using the image processing method. To measure the vibrations visually, the measurement system consists of a camera, an image grabber board, and a computer. The specific target installed on the structure is used to calculate the vibration of structure. The captured image is then converted into a pixel-based data and then analyzed numerically. The limitation of the system depends on the image capturing speed and the size of image. In this paper, we discuss the methodology for the vibration measurement using the image processing method. The method enables us to measure the displacement directly without any contact. The resolution of the vibration measurement can be refined but limited to the sub centimeter displacement.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.3
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• pp.35-46
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• 1992

Recently, the fuzzy theory has been adopted broadly to the applications of image processing. Especially the fuzzy clustering algorithm is adopted to image segmentation to reduce the ambiguity and the influence of noise in an image.But this needs lots of memory and execution time because of the great deal of image data. Therefore a new image segmentation algorithm is needed which reduces the memory and execution time, doesn't change the characteristices of the image, and simultaneously has the same result of image segmentation as the conventional fuzzy clustering algorithm. In this paper, for image segmentation, an extended fuzzy clustering algorithm is proposed which uses the occurence of data of the same characteristic value as the weight of the characteristic value instead of using the characteristic value directly in an image and it is proved the memory reduction and execution time reducted in comparision with the conventional fuzzy clustering algorithm in image segmentation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1255-1261
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• 2020

Currently, the spread of mobile devices is gradually increasing. Accordingly, various techniques using images or photos are actively being researched. However, image data generates noise for complex reasons, and the accuracy of image processing increases according to the performance of removing noise. Therefore, noise reduction is one of the essential steps. Salt and pepper noise is a typical impulse noise in the image, and various studies are being conducted to remove the noise. However, existing algorithms have poor noise rejection performance in high frequency areas, and average filters have blurring. Therefore, in this paper, we propose an algorithm that effectively removes salt and pepper noise in the high frequency region as well as the low frequency region using entropy. For objective and accurate judgment of proposed algorithms, MSE and PSNR were used to compare and analyze existing algorithms.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.27-30
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• 2003

In this paper a new approach of digitalized ‘Takbon’ is introduced. By image signal processing, the letters which were written on stones can be deciphered. Epigraphic letter is detected by digital image device, digital camera. The two dimensional digital image is preprocessed because of sensor noise and detective turbulence. Color image is transformed into grey level. The letter image is analyzed in time/frequency domain. By the resultant analysis data decisive functions are calculated. Signal Processing techniques, such as scaling, clipping, digital negative, high/low filter, morphology and so on, provide algorithms that can extract letter from stones.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.9
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• pp.658-665
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• 2003

In this paper, we propose an improved image encryption and the shift-tolerance method in the Fourier space using a virtual phase image. The encrypted image is obtained by the Fourier transform of the product of a phase-encoded virtual image, not an original image, and a random phase image. Therefore, even if unauthorized users analyze the encrypted image, we can prevent the possibility of counterfeiting from unauthorized people using virtual image which dose not contain any information from the original image. The decryption technique is simply performed by inverse Fourier transform of the interference pattern between the encrypted image and the Fourier decrypting key, made of proposed phase assignment rule, in frequency domain. We demonstrate the robustness to noise, to data loss and shift of the encrypted image or the Fourier decryption key in the proposed technique.