• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.2
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• pp.376-381
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• 2009

The image data compression, recognition, restoration, etc. are parts of the digital image processing technology. In the process by various devices, noises would be made. Because the noise could damage the image, we use the image filter to preserve the original image from the noise. The image filter used in digital image process basically has a two-dimensional structure. There an two methods of creating the filter - One is reiterating one dimension and the other is using an indivisible two-dimension image filter. The image filter is being widely used along with one-dimension filter according to each noise, and various median filters are being used to remove the impulse noise. In this paper, I suggested a powerful modified median filter, and compared with conventional filters for objective verification.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.6
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• pp.302-308
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• 2001

SVD(Singular Value Decomposition) filter for the suppression of EMG in multi-lead stress ECG is studied. SVD filter consists of two parts. In the first part, the basis vectors were chosen from the averaged singular vectors obtained from the decomposed noise-free ECG. The singular vector is computed from the stress ECG and is compared itself with basis vectors to know whether the noise exist in stress ECG. In the second part, the existing elimination method is used, when one(or two) channels is(or are) contaminated by noise. But the proposed enhanced SVD filter is used in case of having the noise in the many channels. During signal decomposition and reconstruction, the noise-free channel or the least noisy channel have the weight of 1, the next less noisy channel has the weight of 0.8. In this way, every channel was weighted by decreased of 0.2 in proportion to the amount of the added noise. For the evaluation of the proposed enhanced SVD filter, we compared the SNR computed by the enhanced SVD filter with the standard average filter for the noise-free signal added with artificial noise and the patient data. The proposed SVD filter showed better in the SNR than the standard average filter. In conclusion, we could find that the enhanced SVD filter is more proper in processing multi-lead stress ECG.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.5
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• pp.1172-1179
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• 1996

General adaptive noise canceller(ANC) suffers from the misadjustment of adaptive filter weights, because of the gradient-estimate noise at steady state. In this paper, an adaptive noise cancellation algorithm with speech detector which is distinguishing speech from silence and adaptation-transient region is proposed. The speech detector uses property of adaptive prediction-error filter which can filter the highly correlated speech. To detect speech region, estimation error which is the output of the adaptive filter is applied to the adaptive prediction-error filter. When speech signal apears at the input of the adaptive prediction-error filter. The ratio of input and output energy of adaptive prediction-error filter becomes relatively lower. The ratio becomes large when the white noise appears at the input. So the region of speech is detected by the ratio. Sign algorithm is applied at speech region to prevent the weights from perturbing by output speech of ANC. As results of computer simulation, the proposed algorithm improves segmental SNR and SNR up to about 4 dBand 11 dB, respectively.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.4
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• pp.223-229
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• 2017

An angle error is a factor obstructing to track accurate position in tracking radars. And the noise incurring the angle error can be divided as follows; thermal noise and glint. In general, Extended Kalman filter used in tracking radars is designed with considering thermal noise only. The Extended Klaman filter uses a fixed measurement error covariance when updating an estimate state by using ahead state and measurement. But, a noise power varies according to the range. Therefore we purposes the adaptive Kalman filter which changes the measurement noise covariance according to the range. In this paper, we compare the performance of the Extended Kalman filter and the proposed adaptive Kalman filter by considering KSLV-I (Korean Satellite Launch Vehicles).

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.3
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• pp.41-48
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• 2012

In this paper, we propose a method of edge detection for noisy image. The proposed method uses a progressive filter for noise reduction and a Sobel operator for edge detection. The progressive filter combines a median filter and a modified rational filter. The proposed method for noise reduction adjusts rational filter direction according to an edge in the image which is obtained by median filtering. Our method effectively attenuates the noise while preserving the image details. Edge detection is performed by a Sobel operator. This operator can be implemented by integer operation and is therefore relatively fast. Our proposed method not only preserves edge, but also reduces noise in uniform region. Thus, edge detection is well performed. Our proposed method could improve results using further developed Sobel operator. Experimental results show that our proposed method has better edge detection with correct positions than those by existing median and rational filtering methods for noisy image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.237-243
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• 2015

In recent image processing, active researches have been made along with rapid development in digital times. However, it is know that the image degradation occurs due to various external factors in the processes of image data processing, transmission and storage, and the main reason of image degradation is due to the noise. Typical methods to remove the noise are CWMF(center weighted median filter), A-TMF(alpha-trimmed mean filter) and AWMF(adaptive weighted median filter) and these methods have a little bit lacking noise reduction characteristics in mixed noise environments. Therefore, in order to remove the mixed noise, image restoration filter processing algorithm was suggested in this paper which processes by applying the median value of the mask and space weighted value after noise judgment. And for the objective judgment, it was compared with existing methods and PSNR(peak signal to noise ratio) was used as a judgment standard.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.10
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• pp.468-476
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• 2002

Digital images are easily corrupted by noise during the data transmission, data capture and data processing. A technical method of noise analyzing and adaptive filtering for reducing of quantum noise in medical radio-fluoroscopy images is presented. By adjusting the characteristics of the filter according to local statistics around each pixel of the image as moving windowing, it is possible to suppress noise sufficiently while preserve edge and other significant information required in diagnosis. We proposed adaptive weighed median(AWM) filters based on local statistics. We showed two ways of realizing the AWM filters. One is a simple type of AWM filter, which is constructed by Homogeneous factor(HF). Homogeneous factor(HF) from the noise models that enables the filter to recognize the local structures of the image is introduced, and an algorithm for determining the HF fitted to the diagnostic systems with various inner statistical properties is proposed. We show by the experimented that the performances of proposed method is superior to these of other filters and models in preserving small details and suppressing the noise at homogeneous region. The proposed algorithms were implemented by Visual C++ language on a IBM-PC Pentium 550 for testing purposes and the effects and results of the filter in the various levels of noise and images were proposed by comparing the values of NMSE(normalized mean square error) with the value of the other existing filtering methods.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.72-80
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• 1996

In this paper, variable windowing mean filter to remove speckle noise and a measure to detect thin edge in ultrasonic images are proposed. Because ultrasonic images are corrupted by speckle noise showing a granular appearance, good edge detection is difficult. As a result, noise removing filter is needed in preprocessing stage. The speckle noise removing filter is based on mean filter whose window size is changed by the ratio of standard deviation to mean for image signal and noise signal in local area. And the measure expressed the difference of means between tow windows is used for detecting thin edge in filtered image. Results show that variable windowing mean filter removes speckle noise effectively, and proposed measure is useful in detecting thin edge.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2701-2706
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• 2009

There are Impulse noise and AWGN in a general image processing. Various methods have been proposed to remove these noises. Well-known filters are Mean, Min-max and Median filter and these show different characteristics depending on the noises. When Impulse noise and AWGN are in superposition environment, single filter doesn't remove noises well. Therefore in this paper, we suggested a switching filter using a probability of noise to restore images in this environment. And we compared a filter with conventional method through simulations.

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