• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.5
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• pp.24-32
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• 2010

In this study, we have proposed a pre-processing algorithm newly developed for improving the tracking efficiency of the optical flow method. The developed pre-processing algorithm consists of a median filter, binarization, morphology, canny edge, contour detecting and an approximation method. In order to evaluate whether the optical flow tracking capacity increases, this study applied the pre-processing algorithm to the Lucas-Kanade(LK) optical flow algorithm, and comparatively analyzed its images and tracking results with those of optical flow without the pre-processing algorithm and with the existing pre-processing algorithm(composed of median filter and histogram equalization). As a result, it was observed that the tracking performance derived from the LK optical flow algorithm with the pre-processing algorithm, shows better tracking accuracy, compared to the one without the pre-processing algorithm and the one with the existing pre-processing algorithm. It seems to have resulted by successful segmentation for characteristic areas and subdivision into inner and outer contour lines.

• Journal of Korea Multimedia Society
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• v.15 no.12
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• pp.1409-1416
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• 2012

An image segmentation result depends on pre-processing steps such as contrast enhancement, edge detection, and smooth filtering etc. Especially medical images are low contrast and contain some noises. Therefore, the contrast enhancement and noise removal techniques are required in the pre-processing. In this study, we present an extension by a novel histogram equalization in which both local and global contrast is enhanced using neighborhood metrics. When checking neighborhood information, filters can simultaneously improve image quality. Most important is that original image information can be used for both global brightness preserving and local contrast enhancement, and image quality improvement filtering. Our experiments confirmed that the proposed method is more effective than other similar techniques reported previously.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.41-51
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• 2024

In this paper, we explore the enhancement of target detection accuracy in the guided weapon using deep learning object detection on infrared (IR) images. Due to the characteristics of IR images being influenced by factors such as time and temperature, it's crucial to ensure a consistent representation of object features in various environments when training the model. A simple way to address this is by emphasizing the features of target objects and reducing noise within the infrared images through appropriate pre-processing techniques. However, in previous studies, there has not been sufficient discussion on pre-processing methods in learning deep learning models based on infrared images. In this paper, we aim to investigate the impact of image pre-processing techniques on infrared image-based training for object detection. To achieve this, we analyze the pre-processing results on infrared images that utilized global or local information from the video and the image. In addition, in order to confirm the impact of images converted by each pre-processing technique on object detector training, we learn the YOLOX target detector for images processed by various pre-processing methods and analyze them. In particular, the results of the experiments using the CLAHE (Contrast Limited Adaptive Histogram Equalization) shows the highest detection accuracy with a mean average precision (mAP) of 81.9%.

• Journal of Broadcast Engineering
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• v.12 no.5
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• pp.516-524
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• 2007

In order to use limited frequency resources efficiently, a single frequency network using digital on-channel repeater(DOCR) has been studied and would be implemented. The DOCR generates strong pre-ghosts to ATSC DTV receivers. The forward filter of equalizer in ATSC DTV receivers compensates the distortion made by pre-ghosts. This process induces noise enhancement and colored noise, thereby results in the performance degradation. In this paper we propose to use a dual-feedback equalizer to combat strong pre-ghosts. The proposed equalizer has two feedback filters. One is the decision feedback filter and the other is non-decision feedback filter. The additional non-decision feedback filter decreases the noise by whitening the noise and preventing the generation of colored noise in pre-ghost channel. Thus the equalization technique of dual-feedback structure has performance enhancement in pre-ghost channel in comparison with conventional decision feedback equalizer(DFE). By simulation we analyzed the performance enhancements of DTV receiver using dual-feedback equalization structure.

• The Journal of the Acoustical Society of Korea
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• v.27 no.3
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• pp.154-162
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• 2008

Signals in an underwater channel get distorted by multipath propagation. In this paper, pre-coding method is suggested which helps comprehending the signals with minimum equalization. The signals are transformed based on the knowledge of the impulse response of the channel. Proposed pre-coding method is tested by simulations based on the ray theory and through water tank experiments. In weak multipath environment, in case of an SNR of about 20 dB, BER is $10^{-3}{\sim}10^{-4}$, while in strong multipath environment, similar BER is achieved with SNR of about 30 dB. In order for the pre-coding method to be used for underwater vehicles, channel prediction method utilizing the waveguide invariant is suggested and tested.

• Journal of radiological science and technology
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• v.44 no.5
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• pp.473-480
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• 2021

This study was purpose to method that applies for improving the image quality in CT and X-ray scan, especially in the lung region. Also, we researched the parameters of the image before and after applying for Histogram Equalization (HE) such as mean, median values in the histogram. These techniques are mainly used for all type of medical images such as for Chest X-ray, Low-Dose Computed Tomography (CT). These are also used to intensify tiny anatomies like vessels, lung nodules, airways and pulmonary fissures. The proposed techniques consist of two main steps using the MATLAB software (R2021a). First, the technique should apply for the process of normalization for improving the basic image more correctly. In the next, the technique actively rearranges the intensity of the image contrast. Second, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method was used for enhancing small details, textures and local contrast of the image. As a result, this paper shows the modern and improved techniques of HE and some advantages of the technique on the traditional HE. Therefore, this paper concludes that various techniques related to the HE can be helpful for many processes, especially image pre-processing for Machine Learning (ML), Deep Learning (DL).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.6A
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• pp.623-630
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• 2004

The adaptive modulation along with SFBC transmit diversity is a very effective method to increase the capacity of an OFDM system. However, severe performance degradation is resulted when inter-symbol interference is applied due to frequency-selective fading in mobile communications. In this paper, we have proposed and analyzed an OFDM system with SFBC transmit diversity and adaptive modulation scheme based on pre-equalization methods, in order to increase the data transmission rate in the downlink without much increase in system complexity. By introducing subchannel grouping and the pre-equalization method among adjacent subchannels, we could enhance the efficiency of the adaptive modulation a lot. By computer simulation, it has been proven that the proposed schemes show a better BER and throughput performance than the conventional schemes under severely time-varying multipath fading channel.

• Journal of Korea Multimedia Society
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• v.14 no.8
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• pp.965-980
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• 2011

In this paper, the use of conditional Fuzzy C-Means (CFCM) aimed at estimation of desired states of an unknown digital communication channel is investigated for blind channel equalization. In the proposed CFCM, a collection of clustered centers is treated as a set of pre-defined desired channel states, and used to extract channel output states. By considering the combinations of the extracted channel output states, all possible sets of desired channel states are constructed. The set of desired states characterized by the maximal value of the Bayesian fitness function is subsequently selected for the next fuzzy clustering epoch. This modification of CFCM makes it possible to search for the optimal desired channel states of an unknown channel. Finally, given the desired channel states, the Bayesian equalizer is implemented to reconstruct transmitted symbols. In a series of simulations, binary signals are generated at random with Gaussian noise, and both linear and nonlinear channels are evaluated. The experimental studies demonstrate that the performance (being expressed in terms of accuracy and speed) of the proposed CFCM is superior to the performance of the existing method exploiting the "conventional" Fuzzy C-Means (FCM).

• Journal of information and communication convergence engineering
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• v.8 no.6
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• pp.609-617
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• 2010

This paper is concerned with the use of a selforganizing map (SOM) to estimate the desired channel states of an unknown digital communication channel for blind equalization. The modification of SOM is accomplished by using the Bayesian likelihood fitness function and the relation between the desired channel states and channel output states. At the end of each clustering epoch, a set of estimated clusters for an unknown channel is chosen as a set of pre-defined desired channel states, and used to extract the channel output states. Next, all of the possible desired channel states are constructed by considering the combinations of extracted channel output states, and a set of the desired states characterized by the maximal value of the Bayesian fitness is subsequently selected for the next SOM clustering epoch. This modification of SOM makes it possible to search the optimal desired channel states of an unknown channel. In simulations, binary signals are generated at random with Gaussian noise, and both linear and nonlinear channels are evaluated. The performance of the proposed method is compared with those of the "conventional" SOM and an existing hybrid genetic algorithm. Relatively high accuracy and fast search speed have been achieved by using the proposed method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2005.11a
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• pp.77-80
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• 2005

본 논문에서는 ATSC 지상파 디지털 TV 방송방식을 사용하는 등화형 동일채널 중계기(Equalization Digital On-Channel Repeater: EDOCR)에서의 신호처리 시간지연을 최소화하기 위한 전치등화 방법에 대하여 기술하고 결과를 다양한 각도에서 분석한다. 제안된 전치등화 방법은 기준신호인 기저대역 신호와 복조된 중계기 출력신호를 이용하여 전치등화필터계수를 계산하고 펄스성형필터계수와의 컨볼루션을 통해 얻어진 새로운 펄스성형필터를 이용하는 것이다. 새로운 펄스성형필터의 전치 탭 수를 조절함으로써 시간지연을 최소화하고, 마스크필터에 의한 선형왜곡을 보상할 뿐 아니라, 원래 펄스성형필터의 대역 외 방사 특성의 열화를 최소화할 수 있다. 이를 컴퓨터 시뮬레이션 및 제작된 전치등화기를 이용하여 검증하였다.