• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.105-107
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• 2012

본 논문에서는 영상의 잡음제거에 주로 적용되어 왔던 양방향 필터(bilateral filter) 기법을 개량하여 에지 정보를 더 잘 살리게 하는 방법을 제안한다. 우선, 잡음 영상에서 에지의 위치를 파악하기 위한 방법으로, 이웃픽셀값들의 분산을 이용하는 방법을 제안한다. 또한 에지와의 거리를 기반으로 필터의 계수를 조정하는 방법을 제시한다. 따라서 제안하는 알고리즘을 적용하여 잡음 제거를 수행하면 기존의 잡음 제거율을 유지하면서도 에지정보를 보존한 결과를 얻을 수 있다.

• ETRI Journal
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• v.11 no.1
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• pp.41-58
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• 1989

Neural network연구는 뇌로부터 얻은 아이디어를 공학적으로 응용하려는 생각을 바탕으로 뇌의 구조와 유사한 mechanism에 의한 정보처리장치의 기초가 되는 정보처리의 양식 확립과 함께 그 정보처리 양식을 구체적으로 각각의 정보처리 문제에 응용하기 위한 응용기술을 연구하는 것이다. Neural network의 계산 기능적 특성은 병렬처리, 학습 및 noisy한 정보의 효율적처리 등으로써 특히 pattern인식 문제에 효율적으로 응용될 수 있다. 본 논문에서는 neural network의 역사적 고찰과 기존의 model들을 살펴보고 새로운 계산 구조와 계산 방식을 가진 neural network의 응용분야를 살펴 봄으로써 기존의 AI 기법으로 해결하기 어려운 pattern recognition(image,문자,speech등), robot vision 및 control 등 여러가지 문제에 효율적으로 적용가능함과 neural network의 앞으로의 전망에 대하여 기술한다.

• Journal of Electrical Engineering and information Science
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• v.1 no.2
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• pp.27-32
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• 1996

In target tracking area, the data association plays an important role and has been studied extensively. In this paper, after defining the data association as a constrained optimization, we introduce a new energy function and thereby an efficient realization of neural networks. As an application, this algorithm is used to detect object boundaries in IR images. The problem is that the IR image noisy, the shape of the object is variable, and the positions of the end points are not predictable. The performance of this algorithm is discussed with the experimental results.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.414-417
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• 2007

In this paper we present an adaptive method for accelerating conventional Maximum Entropy Method (MEM) for restoration of Passive Millimeter-Wave (PMMW) image from its blurred and noisy version. MEM is nonlinear and its convergence is very slow. We present a new method to accelerate the MEM by using an exponent on the correction ratio. In this method the exponent is computed adaptively in each iteration, using first-order derivatives of deblurred image in previous two iterations. Using this exponent the accelerated MEM emphasizes speed at the beginning stages and stability at later stages. In accelerated MEM the non-negativity is automatically ensured and also conservation of flux without additional computation. Simulation study shows that the accelerated MEM gives better results in terms of RMSE, SNR, moreover, it takes only about 46% lesser iterations than conventional MEM. This is also confirmed by applying this algorithm on actual PMMW image captured by 94 GHz mechanically scanned radiometer.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.339-348
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• 1997

In this paper, we propose a coding method to improve compression efficiency for MR image. This can be achieved by combining coding scheme and segmentation scheme which removes noisy background region, which is meaningless for diagnosis in the MR image. In segmentation algoritm, we use full-resolution wavelet transform to extract features of regions in image and Kohonen self-organizing map to classify the features. The subsequent wavelet coder encodes only diagnostically significant foreground regions refering to segmentation map. Our proposed algorithm provides about 15% of bit rate reduction when compared with the same coder which is not combined with segmentation scheme. And the proposed scheme shows better reconstructed image quality than JPEG at the same compression ratio.

• Journal of the Semiconductor & Display Technology
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• v.18 no.3
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• pp.66-71
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• 2019

In shape from focus (SFF) methods, the quality of image focus volume plays a vital role in the quality of 3D shape reconstruction. Traditionally, a linear 2D filter is applied to each slice of the image focus volume to rectify the noisy focus measurements. However, this approach is problematic because it also modifies the accurate focus measurements that should ideally remain intact. Therefore, in this paper, we propose to enhance the focus volume adaptively by applying 3-dimensional weighted least squares (3D-WLS) based regularization. We estimate regularization weights from the guidance volume extracted from the image sequences. To solve 3D-WLS optimization problem efficiently, we apply a technique to solve a series of 1D linear sub-problems. Experiments conducted on synthetic and real image sequences demonstrate that the proposed method effectively enhances the image focus volume, ultimately improving the quality of reconstructed shape.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4
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• pp.70-76
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• 1996

In the case of an image corrupted with mixed noise, conventional MMSE filter can not remove such a mixed noise properly, because the impulse moise cause a certain bias of the minimum mean-square error estimate at regions close to outliers. In this paper, we proposed the new method or removal of mixed noise by combining MMSE filtering structure with local multi-windowing method according to directions and with ranked-order method. As a result, the improvement of the image quality with the proposed was obtained between about 9.7 and 35.2 times in the sense of NMSE(normalized mean square errors) evaluation than that of MMSE filter. Also, we could obtain the enhanced image in the mixed noisy image from visual and quantitative aspect.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.479-490
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• 2003

This study proposed a modified anisotropic diffusion restoration for image classification. The anisotropic diffusion restoration uses a probabilistic model based on Markov random field, which represents geographical connectedness existing in many remotely sensed images, and restores them through an iterative diffusion processing. In every iteration, the bonding-strength coefficient associated with the spatial connectedness is adaptively estimated as a function of brightness gradient. The gradient function involves a constant called "temperature", which determines the amount of discontinuity and is continuously decreased in the iterations. In this study, the proposed method has been extensively evaluated using simulated images that were generated from various patterns. These patterns represent the types of natural and artificial land-use. The simulated images were restored by the modified anisotropic diffusion technique, and then classified by a multistage hierarchical clustering classification. The classification results were compared to them of the non-restored simulation images. The restoration with an appropriate temperature considerably reduces error in classification, especially for noisy images. This study made experiments on the satellite images remotely sensed on the Korean peninsula. The experimental results show that the proposed approach is also very effective on image classification in remote sensing.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.222-228
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• 2023

This study aimed to analyze the applicability of the improved median filter in positron emission tomography (PET)/magnetic resonance (MR) fusion images based on parallel imaging using generalized autocalibrating partially parallel acquisition (GRAPPA). In this study, a PET/MR fusion imaging system based on a 3.0T magnetic field and ¹⁸F radioisotope were used. An improved median filter that can set a mask of the median value more efficiently than before was modeled and applied to the acquired image. As quantitative evaluation parameters of the noise level, the contrast to noise ratio (CNR) and coefficient of variation (COV) were calculated. Additionally, no-reference-based evaluation parameters were used to analyze the overall image quality. We confirmed that the CNR and COV values of the PET/MR fusion images to which the improved median filter was applied improved by approximately 3.32 and 2.19 times on average, respectively, compared to the noisy image. In addition, the no-reference-based evaluation results showed a similar trend for the noise-level results. In conclusion, we demonstrated that it can be supplemented by using an improved median filter, which suggests the problem of image quality degradation of PET/MR fusion images that shortens scan time using GRAPPA.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.410-418
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• 2023

Recently, advancements and commercialization in the field of maritime autonomous surface ships (MASS) has rapidly progressed. Concurrently, studies are also underway to develop methods for automatically surveying the condition of various on-board equipment remotely to ensure the navigational safety of MASS. One key issue that has gained prominence is the method to obtain values from analog gauges installed in various equipment through image processing. This approach has the advantage of enabling the non-contact detection of gauge values without modifying or changing already installed or planned equipment, eliminating the need for type approval changes from shipping classifications. The objective of this study was to identify a dynamically changing indicator needle within noisy images of analog gauges. The needle object must be identified because its position significantly affects the accurate reading of gauge values. An analog pressure gauge attached to an emergency fire pump model was used for image capture to identify the needle object. The acquired images were pre-processed through Gaussian filtering, thresholding, and morphological operations. The needle object was then identified through Hough Transform. The experimental results confirmed that the center and object of the indicator needle could be identified in images of noisy analog gauges. The findings suggest that the image processing method applied in this study can be utilized for shape identification in analog gauges installed on ships. This study is expected to be applicable as an image processing method for the automatic remote survey of MASS.