• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.2
• /
• pp.27-33
• /
• 2022

The small infrared image homing sensor is the eye of a guided weapon that has an infrared image sensor that identifies a target on the ground through day and night infrared image processing and searches, detects, and tracks the target. Inside the guided weapon since the power supply and communication line are used together with various components, the part against electromagnetic wave interference is very important. In particular, the effect of CE (Conducted Emission) through the power and communication lines connected by cables is very important. Through this method, it is possible to directly affect other components of the guided weapon. In this paper, the EMI filter and cable design for avoiding electromagnetic interference to the power input through the cable and the communication line are described. Also, the designed EMI filter is manufactured After the CE102 test of MIL-STD-461G, design satisfaction will be explained.

• Korean Journal of Remote Sensing
• /
• v.34 no.5
• /
• pp.777-786
• /
• 2018

GF (Guided Filtering) is a representative image processing technique to effectively remove noise while preserving edge information in the digital image. In this paper, we proposed a unsupervised change detection method for the KOMPSAT-3 satellite image using the GF and evaluated its performance. In order to utilize GF for the unsupervised change detection, cross-sharpened images were generated based on GF, and CVA (Change Vector Analysis) was applied to the generated cross-sharpened images to extract the changed area in the multitemporal satellite imagery. Experimental results using KOMPSAT-3 satellite images showed that the proposed method can be effectively used to detect changed regions compared with CVA results based on existing cross-sharpened images.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.10
• /
• pp.5112-5129
• /
• 2019

Image enhancement is a challenging problem in the field of image processing, especially low-light color images enhancement. This paper proposed a robust and comprehensive enhancement method based several points. First, the idea of bright channel is introduced to estimate the illumination map which is used to attain the enhancing result with Retinex model, and the color constancy is keep as well. Second, in order eliminate the illumination offsets wrongly estimated, morphological closing operation is used to modify the initial estimating illumination. Furthermore, in order to avoid fabricating edges, enlarged noises and over-smoothed visual features appearing in enhancing result, a multi-scale closing operation is used. At last, in order to avoiding the haloes and artifacts presented in enhancing result caused by gradient information lost in previous step, guided filtering is introduced to deal with previous result with guided image is initial bright channel. The proposed method can get good illumination map, and attain very effective enhancing results, including dark area is enhanced with more visual features, color natural and constancy, avoiding artifacts and over-enhanced, and eliminating Incorrect light offsets.

• Journal of Digital Contents Society
• /
• v.17 no.6
• /
• pp.523-529
• /
• 2016

Stereo matching algorithm is an essential part in computer vision and photography. Accuracy and computational complexity are challenges of stereo matching algorithm. Much research has been devoted to stereo matching based on cost volume filtering of matching costs. Local stereo matching based guided image filtering (GIF) has a computational complexity of O(N), but is still not enough to provide real-time 3-dimensional (3-D) video services. The proposed algorithm concentrates reduction of computational complexity using the concept of fast guided image filter, which increase the speed up to $O(N/\small{s}^2)$ with a sub-sampling ratio $\small{s}$. Experimental results indicated that the proposed algorithm achieves effective local stereo matching as well as a fast execution time for 3-D video service.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.2
• /
• pp.124-132
• /
• 2014

In this paper, we present a novel backlit region detection and contrast enhancement method using fuzzy C-means clustering and adaptively partitioned block based contrast stretching. The proposed method separates an image into both dark backlit and bright background regions using adaptively partitioned blocks based on the optimal threshold value computed by fuzzy logic. The detected block-wise backlit region is refined using the guided filter for removing block artifacts. Contrast stretching algorithm is then applied to adaptively enhance the detected backlit region. Experimental results show that the proposed method can successfully detect the backlit region without a complicated segmentation algorithm and enhance the object information in the backlit region.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2017.11a
• /
• pp.88-91
• /
• 2017

넓은 동적 영역 (high dynamic range: HDR) 이미지는 시각적으로 우수하지만 대부분의 디스플레이는 좁은 동적 영역 (low dynamic range: LDR)만 지원이 가능하다. 이를 해결하기 위해서 톤 매핑 기법 (tone mapping operator: TMO)을 사용한 동적 영역 압축을 수행한다. 기존의 적응적 로그 매핑 (adaptive logarithmic mapping)의 경우 에지 부분에서 디테일이 손실되는 문제점이 있었다. 본 논문에서는 가이디드 이미지 필터링 (guided image filtering: GIF)을 통해 베이스 레이어와 디테일 레이어로 나눠서 처리하는 알고리듬을 제안한다. 베이스 레이어는 적응적 로그 매핑을 통해 동적 영역을 압축하고 디테일 레이어와 더해 기존의 톤 매핑 과정에서 발생하는 디테일의 손실을 감소시켰다.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.33 no.3
• /
• pp.264-269
• /
• 2013

Since MFC(macro-fiber composite) transducer has been developed, many researchers have tried to apply this transducer on SHM(structural health monitoring), because it is so flexible and durable that it can be easily embedded to various kinds of structures. The objective of this paper is to figure out the benefits and feasibility of applying MFC transducers to guided wave technique. For this, we have experimentally tested the performance of MFC patches as transmitter and sensors for excitation and reception of guided waves on the thin aluminum alloy plate. In order to enhance the signal accuracy, we applied the FIR filter for noise reduction as well as used STFT(short-time Fourier transform) algorithm to image the guided wave characteristics clearly. From the results, the guided wave generated based on MFC showed good agreement with its theoretical dispersion curves. Moreover, the ultrasonic Lamb wave techniques based on MFC patches in pitch-catch manner was tested for detection of surface notch defects of which depths are 10%, 20%, 30% and 40% of the aluminum plate thickness. Results showed that the notch was detectable well when the notch depth was 10% of the thickness or greater.

• Journal of Mechanical Science and Technology
• /
• v.15 no.8
• /
• pp.1097-1107
• /
• 2001

In this paper, the wall following navigation algorithm of the mobile robot using a mono vision system is described. The key points of the mobile robot navigation system are effective acquisition of the environmental information and fast recognition of the robot position. Also, from this information, the mobile robot should be appropriately controlled to follow a desired path. For the recognition of the relative position and orientation of the robot to the wall, the features of the corridor structure are extracted using the mono vision system, then the relative position, the offset distance and steering angle of the robot from the wall, is derived for a simple corridor geometry. For the alleviation of the computation burden of the image processing, the Kalman filter is used to reduce search region in the image space for line detection. Next, the robot is controlled by this information to follow the desired path. The wall following control scheme by the PD control scheme is composed of two control parts, the approaching control and the orientation control, and each control is performed by steering and forward-driving motion of the robot. To verify the effectiveness of the proposed algorithm, the real time navigation experiments are performed. Through the result of the experiments, the effectiveness and flexibility of the suggested algorithm are verified in comparison with a pure encoder-guided mobile robot navigation system.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.15 no.2
• /
• pp.102-110
• /
• 2015

The main source of noise in computed tomography (CT) images is a quantum noise, which results from statistical fluctuations of X-ray quanta reaching the detector. This paper proposes a neural network (NN) based hybrid filter for removing quantum noise. The proposed filter consists of bilateral filters (BFs), a single or multiple neural edge enhancer(s) (NEE), and a neural filter (NF) to combine them. The BFs take into account the difference in value from the neighbors, to preserve edges while smoothing. The NEE is used to clearly enhance the desired edges from noisy images. The NF acts like a fusion operator, and attempts to construct an enhanced output image. Several measurements are used to evaluate the image quality, like the root mean square error (RMSE), the improvement in signal to noise ratio (ISNR), the standard deviation ratio (MSR), and the contrast to noise ratio (CNR). Also, the modulation transfer function (MTF) is used as a means of determining how well the edge structure is preserved. In terms of all those measurements and means, the proposed filter shows better performance than the guided filter, and the nonlocal means (NLM) filter. In addition, there is no severe restriction to select the number of inputs for the fusion operator differently from the neuro-fuzzy system. Therefore, without concerning too much about the filter selection for fusion, one could apply the proposed hybrid filter to various images with different modalities, once the corresponding noise characteristics are explored.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.2
• /
• pp.117-123
• /
• 2018

In this study, we suggest a fast image reconstruction scheme using Poisson equation from image gradient domain. In this approach, using the Poisson equation, a guided vector field is created by employing source and target images within a selected region at the first step. Next, the guided vector is used in generating the result image. We analyze the problem of reconstructing a two-dimensional function that approximates a set of desired gradients and a data term. The joined data and gradients are able to work like modifying the image gradients while staying close to the original image. Starting with this formulation, we have a screened Poisson equation known in physics. This equation leads to an efficient solution to the problem in FFT domain. It represents the spatial filters that solve the two-dimensional screened Poisson model and shows gradient scaling to be a well-defined sharpen filter that generalizes Laplace sharpening. We demonstrate the results using a discrete cosine transformation based this Poisson model.