• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1342-1348
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• 2017

In an era where digital data takes on great importance, images are essential to various media. Noise is generated during the acquisition and transmission of such images, caused by a number of external factors. The removal of noise is an essential step in image processing. There are various methods used to remove noise, in accordance with the cause or form of the noise. AWGN is one of the leading methods. As such, this paper applies the edge detection method using the mean of each pixel after categorizing in detail the partial masks into nine areas as part of the preliminary process, in order to minimize noise that had been added to the image. In addition, the paper suggests an algorithm that applies different filters to the partial masks by using the critical mass value of the transfigured edge detection. To verify the competence of the suggested algorithm, it was compared with existing methods by using magnified images and PSNR(peak signal to noise ratio).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.213-215
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• 2021

With the development of IoT technology, various technologies such as artificial intelligence and automation are being grafted into industrial sites, and accordingly, the importance of data processing is increasing. In particular, a system based on a digital image may cause a malfunction due to noise in the image due to a sensor defect or a communication environment problem. Therefore, research on image processing has been continued as a pre-processing process, and an effective noise reduction technique is required depending on the type of noise and the characteristics of the image. In this paper, we propose a modified spatial weight filter to protect edge components in the impulse noise reduction process. The proposed algorithm divides the filtering mask into four regions and calculates the standard deviation of each region. The final output was filtered by applying a spatial weight to the region with the lowest standard deviation value. Simulation was conducted to evaluate the performance of the proposed algorithm, and it showed superior impulse noise reduction performance compared to the existing method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.431-433
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• 2022

With the development of IoT technology, various technologies such as artificial intelligence and automation are being grafted into industrial sites, and accordingly, the importance of data processing is increasing. Image denoising is one of the basic processes of image processing, and is used as a preprocessing step in many applications. Various studies have been conducted to remove noise, but various problems arise in the process of noise removal, such as image detail preservation, texture restoration, and special noise removal. In this paper, we propose a digital filter using an extended convolutional mask to preserve image detail during the impulse denoising process. The proposed algorithm uses an extended convolution mask as a filtering mask, and obtains the final output by switching the extension level according to the noise level. Simulation was conducted to evaluate the performance of the proposed algorithm, and the performance was analyzed compared to the existing method.

• Korean Journal of Geomatics
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• v.4 no.1
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• pp.1-8
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• 2004

In the middle-resolution remote sensing, the Ground Sampled Distance (GSD) that the detector senses and samples is generally larger than the actual size of the objects (or materials) of interest, and so several objects are embedded in a single pixel. In this case, as it is impossible to detect these objects by the conventional spatial-based image processing techniques, it has to be carried out at sub-pixel level through spectral properties. In this paper, we explain the sub-pixel analysis algorithm, also known as the Linear Spectral Mixing (LSM) model, which has been experimented using the Hyperion data. To find Endmembers used as the prior knowledge for LSM model, we applied the concept of the convex geometry on the two-dimensional scatter plot. The Atmospheric Correction and Minimum Noise Fraction techniques are presented for the pre-processing of Hyperion data. As LSM model is the simplest approach in sub-pixel analysis, the results of our experiment is not good. But we intend to say that the sub-pixel analysis shows much more information in comparison with the image classification.

• Journal of KIISE
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• v.42 no.7
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• pp.910-918
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• 2015

This paper presents a parameter visualization technique to overcome the limitation of the naked eye in contrast-enhanced ultrasonography. A method is also proposed to compensate for the distortion and noise in ultrasound image sequences. Meaningful parameters for diagnosing liver disease can be extracted from the dynamic patterns of the contrast enhancement in ultrasound images. The visualization technique can provide more accurate information by generating a parametric image from the dynamic data. Respiratory motions and noise from micro-bubble in ultrasound data may cause a degradation of the reliability of the diagnostic parameters. A multi-stage algorithm for respiratory motion tracking and an image enhancement technique based on the Markov Random Field are proposed. The usefulness of the proposed methods is empirically discussed through experiments by using a set of clinical data.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.526-531
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• 1992

Partial derivatives are easily computed analytically assuming that all the geometric information is known. However, there are computational difficulties in getting accurate partial derivatives directly from a range image since an image is a discrete version of continuous data contaminated with some noise. In this paper, we develop a general window function to compute partial derivatives based on the least square surface fitting method. A dynamic selective surface fitting method is introduced to make the window less sensitive to noise. Any degree of partial derivative can be obtained by a simple convolution between an image and window functions.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.4
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• pp.170-179
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• 1993

In this study, self-organized neural network is used to solve the vorrespondence problem of the axial stereo image. Edge points are extracted from a pair of stereo images and then the edge points of rear image are assined to the output nodes of neural network. In the matching process, the two input nodes of neural networks are supplied with the coordi- nates of the edge point selected randomly from the front image. This input data activate optimal output node and its neighbor nodes whose coordinates are thought to be correspondence point for the present input data, and then their weights are allowed to updated. After several iterations of updating, the weights whose coordinates represent rear edge point are converged to the coordinates of the correspondence points in the front image. Because of the feature map properties of self-organized neural network, noise-free and smoothed depth data can be achieved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3121-3143
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• 2019

Segmentation plays an important role in the field of image processing and computer vision. Intuitionistic fuzzy C-means (IFCM) clustering algorithm emerged as an effective technique for image segmentation in recent years. However, standard fuzzy C-means (FCM) and IFCM algorithms are sensitive to noise and initial cluster centers, and they ignore the spatial relationship of pixels. In view of these shortcomings, an improved algorithm based on IFCM is proposed in this paper. Firstly, we propose a modified non-membership function to generate intuitionistic fuzzy set and a method of determining initial clustering centers based on grayscale features, they highlight the effect of uncertainty in intuitionistic fuzzy set and improve the robustness to noise. Secondly, an improved nonlinear kernel function is proposed to map data into kernel space to measure the distance between data and the cluster centers more accurately. Thirdly, the local spatial-gray information measure is introduced, which considers membership degree, gray features and spatial position information at the same time. Finally, we propose a new measure of intuitionistic fuzzy entropy, it takes into account fuzziness and intuition of intuitionistic fuzzy set. The experimental results show that compared with other IFCM based algorithms, the proposed algorithm has better segmentation and clustering performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.252-255
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• 2014

Image treatment is becoming mainstream as the demand for image restoration has drastically increased in the digital era. But in the process of acquiring, transmitting and treating video data, the salt and pepper noise damages the image. One of the major methods used for restoring images are SMF(standard median filter), CWMF(center weighted median filter) and SWMF(switching weighted median filter), but these filters all leave a bit to be desired in terms of removing noise and preserving edge. Therefore, a transformed median filter is suggested through the algorithm presented for the restoration of damaged images.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.695-702
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• 2010

This paper presents an image alignment algorithm for application of AOI (Automatic Optical Inspection) based on SIFT. Since the correspondences result using SIFT descriptor have many wrong points for aligning, this paper modified and classified those points by five measures called the CCFMR (Cascade Classifier for False Matching Reduction) After reduced the false matching, rotation and translation are estimated by point selection method. Experimental results show that the proposed method has fewer fail matching in comparison to commercial software MIL 8.0, and specially, less than twice with the well-controlled environment’s data sets (such as AOI system). The rotation and translation accuracy is robust than MIL in the noise data sets, but the errors are higher than in a rotation variation data sets although that also meaningful result in the practical system. In addition to, the computational time consumed by the proposed method is four times shorter than that by MIL which increases linearly according to noise.