• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2239-2246
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• 2011

In image signal processing, image signal is corrupted by various noises and caused the degradation phenomenon. And Images often corrupted by AWGN(additive white gaussian noise) and impulse noise which called mixed noise. In this paper, the algorithm is proposed to remove mixed noise while keeping edge information. The proposed algorithm first classifies the noise type, if the classify result is AWGN, then the mean of the output after using self-adaptive weighted mean filter and median value will be the outfiltering value. And if the noise type is impulse noise, then the noise is removed by a modified nonlinear filter. Also we compare existing methods through the simulation and using PSNR(peak signal to noise ratio) as the standard of judgement of improvement effect. The result of computer simulation on test images indicates that the proposed method is superior to traditional filtering algorithms.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.3
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• pp.109-116
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• 2003

In this paper. we Propose an UXGA class LCD controller for controlling the LCD panel. The proposed controller supports the full screen display using GCD between input and output resolutions. The proposed LCD controller includes the motion detector based on median filter which can detect the motion of input image for the enhancement of a image quality. Also, it divides the motion into 3 stages such as still, semi-moving and moving, and uses the different interpolation algorithms according to the degree of motion. In order to evaluate the performance of the proposed interpolation algorithm, we use PSNR method and compare the conventional algorithm by using computer simulation. For the proposed motion detection algorithm, we use a visual verification and the estimation of pixel changes. The proposed LCD controller has been designed and verified by VHDL. It has been synthesized using Xilinx VirtexE FPGA.

• International Journal of Contents
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• v.13 no.3
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• pp.1-8
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• 2017

Rice plant numbers and density are key factors for yield and quality of rice grains. Precise and properly estimated rice plant numbers and density can assure high yield from rice fields. The main objective of this study was to automatically detect and count rice plants using images of usual field condition from an unmanned aerial vehicle (UAV). We proposed an automatic image processing method based on morphological operation and boundaries of the connected component to count rice plant numbers after transplanting. We converted RGB images to binary images and applied adaptive median filter to remove distortion and noises. Then we applied a morphological operation to the binary image and draw boundaries to the connected component to count rice plants using those images. The result reveals the algorithm can conduct a performance of 89% by the F-measure, corresponding to a Precision of 87% and a Recall of 91%. The best fit image gives a performance of 93% by the F-measure, corresponding to a Precision of 91% and a Recall of 96%. Comparison between the numbers of rice plants detected and counted by the naked eye and the numbers of rice plants found by the proposed method provided viable and acceptable results. The $R^2$ value was approximately 0.893.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.1
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• pp.95-101
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• 2007

Recently, two phase scheme for removing impulse noise in images is proposed. This algorithms first detect the noise candidates based on the adaptive median filter, and then apply optimizing techniques recursively only to those noise candidates to restore image. Thus the noise detector with high accuracy is important role on this algorithm, In this paper, novel noise detector is proposed, which can detect impose noise with high accuracy while reducing the probability of false detecting image details as impulses. And the method for reducing computational cost of regularization phase is presented also.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.727-734
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• 2014

Digital processing technologies are being studied in various areas of image compression, recognition and recovery. However, image deterioration still occurs due to the noises in the process of image acquisition, storage and transmission. Generally in the typical noises which are included in the images, there are Gaussian noise and the mixed noise where the Gaussian noise and impulse noise are overlapped and in order to remove these noises, various researches are being executed. In order to preserve the edge and effectively remove mixed noises, image recovery filter algorithm was suggested in this study which sets and processes the adaptive weight using the median values and average values after noise judgment. Additionally, existing methods were compared through simulations and PSNR(peak signal to noise ratio) was used as a judgment standard.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.65-73
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• 2017

In this paper, we propose a stereo matching method to improve the image quality at the hole and the disparity discontinuity regions. The stereo matching method extracts disparity map finding corresponding points between stereo image pair. However conventional stereo matching methods have a problem about the tradeoff between accuracy and precision with respect to the length of the baseline of the stereo image pair. In addition, there are hole and disparity discontinuity regions which are caused by textureless regions and occlusion regions of the stereo image pair. The proposed method extracts initial disparity map improved at disparity discontinuity and miss-matched regions using modified AD-Census-Gradient method and adaptive weighted cost aggregation. And then we conduct the disparity map refinement to improve at miss-matched regions, while also improving the accuracy of the image. Experimental results demonstrate that the proposed method produces high-quality disparity maps by successfully improving miss-matching regions and accuracy while maintaining matching performance compared to existing methods which produce disparity maps with high matching performance. And the matching performance is increased about 3.22(%) compared to latest stereo matching methods in case of test images which have high error ratio.