• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.8
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• pp.1094-1101
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• 1993

We show that image compression possibilities of BDPCM which reduces information and increases correlation with signal-blurring. Under the same quantization steps, signal flow at the coder is analyzed and complete signal reconstruction properties are shown. Dynamic range characteristics of the differences by the conventional DPCM predictor are analyzed. In order to improve the median filter reduces impulse noise with blurring, adaptive filtering for the differences is proposed. By means of the difference range, transmission impulse noises are detected and corrected by the filtering. Therefore, low bit rate image codec with noise eliminations is proposed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.354-357
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• 2013

The filtering algorithm is used very frequently in the preprocessing stage of many image processing algorithms in computer vision processing. Because video signals are two-dimensional signals, computaional complexity is very high. To reduce the complexity, separable filters and the factorization theorem is applied to the filtering operation. As a result, it is shown that a significant reduction in computational complexity is achieved, although the experimental results could be slightly different depending on the condition of the image.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.863-867
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• 1987

When digital image signals are transmitted or stored, they may be usually degraded by impulsive noise such as BSC noise. Though median filtering is a very effective method to reduce the impulsive noise, it brings non-negligible distortion after filtering. Several algorithms have been proposed to reduce such a distortion, but their reconstructed image quality are inadequate in some cases and they have a difficulty in real-time processing. In this paper, an effective filtering algorithm which can not only reduce the noise effectively but also preserve the edges well and lessen the distortion greatly, is presented. The proposed algorithm is an adaptive algorithm of median filter using local statistics, based on the characteristics of human eyes. The adaptive algorithm results shwo performance improvement of up to 3-4 dB over the nonadaptive one.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.601-607
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• 2018

Median filtering is a nonlinear method which is known to be effective in removing impulse noise while preserving local image structure relatively well. However, it could still suffer the smearing phenomena of edges and fine details into neighbors due to undesirable influence from the pixels whose values are far off from the true value of the pixel at hand. This drawback mainly comes from the fact that median filters typically employ a regular shape window for collecting the pixels used in the filtering operation. In this paper, we propose a median filtering method which employs an irregular shape filter window in collecting neighboring pixels around the pixel to be denoised. By employing an irregular shape window, we can achieve good noise suppression while preserving image details. Experimental results have shown that our approach is superior to regular window-based methods.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.930-932
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• 2003

For this study we used a CCD video camera to capture the pavement image information via the computer. During investigation processing, the CCD video camera captured 10${\sim}$30 images per second. If the vehicle velocity is too fast, the collected images will be duplicated and if the velocity is too slow there will be a gapped between images. Therefore, in order to control the efficiency of the image grabber we should add accessory tools such as the Differential Global Positioning System (DGPS) and odometer. Furthermore, Kalman Filtering can also solve these problems. After the CCD video camera captured the pavement images, we used the Least-Squares method to eliminate images of gradation which have non-uniform surfaces due to the illumination at night. The Fuzzy Entropy method calculates images of threshold segments and creates binary images. Finally, the Object Labeling algorithm finds objects that are cracks or noises from the binary image based on volume pixels of the object. We used these algorithms and tested them, also providing some discussion and suggestions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.7
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• pp.1001-1007
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• 2014

This paper proposes a method to reduce operation time using discrete cosine transform and to improve image quality by the DC gain correction. Conventional filtered back projection (FBP) filtering in the frequency domain using Fourier transform, but the filtering process uses complex number operations. To simplify the filtering process, we propose a filtering process using discrete cosine transform. In addition, the image quality of reconstructed images are improved by correcting DC gain of sinograms. To correct the DC gain, we propose to find an optimum DC weight is defined as the ratio of sinogram DC and optimum DC. Experimental results show that the proposed method gets better performance than the conventional method for phantom and clinical CT images.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.487-491
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• 2009

The inverse halftoning is processing to restore the image made binary to former step image. There are smoothing and gaussian filtering in the technique so far. However, there are still a lot of insufficient points in past inverse halftoning. The removal of the noise and the edge enhansment are closely related in inverse halftoning. It is difficult to do both the noise rednctiom and the edge enhansment in high accuracy at the same time in the technique so far. The technique that can achieve both the removal of the noise and the emphasis of Edge at the same time is expected as future tasks. Then, it was tried to apply the Kalmann filtering to inverse halftoning. In the actual experiment, the effectiveness of the application of the Kalmann filtering to inverse halftoning comparing it with the technique so far was shown.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.13-18
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• 2012

In this paper, we propose a noise estimation method for noise reduction. It is based on block and pixel-based noise estimation. We assume that an input image is contaminated by the additive white Gaussian noise. Thus, we use an adaptive Gaussian filter and estimate the amount of noise. It computes the standard deviation of each block and estimation is performed on pixel-based operation. The proposed algorithm divides an input image into blocks. This method calculates the standard deviation of each block and finds the minimum standard deviation block. The block in flat region shows well noise and filtering effects. Blocks which have similar standard deviation are selected as test blocks. These pixels are filtered by adaptive Gaussian filtering. Then, the amount of noise is calculated by the standard deviation of the differences between noisy and filtered blocks. Experimental results show that our proposed estimation method has better results than those by existing estimation methods.

• Fire Science and Engineering
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• v.19 no.4 s.60
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• pp.64-68
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• 2005

In this paper, we studied fire position recognition and alarm system when we acquired CCDcamera image of fire region and position. We proposed effectively extraction system of boundary of fire region using RED Filtering, and applied 2-graylevel image method to fire boundary extraction. Finally we can make system of fire position and region using mask extraction and matching for fire recognition. For the purpose of experiment result, we effectively recognized that the tire occurrence position and region have steadily spread.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.236-237
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• 2019

In this paper, we propose a wavelet - based image noise reduction algorithm. We develop wavelet transform of existing Mallat Tree method. First, we propose a multiple filtering method. Maximizes the energy concentration characteristic of the wavelet transform considering the energy of each subband in the wavelet domain. We apply the proposed multiple filtering to the noise image. Finds energy subbands that can not be seen in normal images and removes them to remove noise.