• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.151-156
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• 2010

This paper is a study on image noise reduction of modified sigma filter by image decomposition using directivity. Conventional sigma filter has been shown to be a good solution both in terms of filtering accuracy and computational complexity. However, the sigma filter does not preserve well small edges especially for high level of additive noise. In this paper, we propose here a new method using a modified sigma filter. In our proposed method the input image is first decomposed in two components that have features of horizontal, vertical and diagonal direction. Then, two components are applied HPF and LPF. By applying a conventional sigma filter separately on each of them, the output image is reconstructed from the filtered components. Added noise is removed and our proposed method preserves the edges from the image. Comparative results from experiments show that the proposed algorithm achieves higher gains, on average, 2.6 dB PSNR than the sigma filter and 0.5 dB PSNR than the modified sigma filter. When relatively high levels of noise added, the proposed algorithm shows better performance than two conventional filters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.10
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• pp.2261-2268
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• 2010

The information captured by imaging devices such as CCD or CIS may contain external noises through the processes of passing signals or storing images. In this paper, we propose a Modified Sigma Filter (MSF) algorithm to reduce such noises. In experiment, we verified that our MSF algorithm showed better performance in PSNR and 1D plot of simulation results compared with Gaussian Filter (GF), Local Sigma Filter (LSF). Tested images include random Gaussian Noises.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2649-2655
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• 2015

While the oversampling sigma-delta ADCs are known to have high resolution, they often suffer from SNDR losses when operated at a very high data clock. This paper presents a design and implementation of a decimation filter that provides minimum distortion at passband for high-speed sigma-delta ADC. The proposed digital decimation filter employs a butterworth structure. To evaluate the performance of the proposed decimation filter, we implemented a 1-bit, third-order, OSR=64 sigma-delta modulator followed by the proposed decimation filter. Using the simulation ad measurement, we compared the performance of the proposed decimation filter with a conventional CIC(cascaded integrator comb) decimation filter, which is commonly used in most sigma-delta ADCs. The measurement results show that the proposed decimation filter presents substantially lower distortion at passband and thus can provide must higher SNDR.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.405-408
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• 2015

While the oversampling sigma-delta ADCs are known to have high resolution, they often suffer from SNDR losses when operated at a very high data clock. This paper presents a design and implementation of a decimation filter that provides minimum distortion at passband for high-speed sigma-delta ADC. The proposed digital decimation filter employs a butterworth structure, which is a type of an IIR filter. To evaluate the performance of the proposed decimation filter, we implemented a 1-bit, third-order, OSR=64 sigma-delta modulator followed by the proposed decimation filter. Using the simulation ad measurement, we compared the performance of the proposed decimation filter with a conventional CIC(cascaded integrator comb) decimation filter, which is commonly used in most sigma-delta ADCs. The measurement results show that the proposed decimation filter presents substantially lower distortion at passband and thus can provide must higher SNDR.

• Journal of IKEEE
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• v.23 no.2
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• pp.461-468
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• 2019

A continuous-time Delta-Sigma ADC has various benefits; it does not require an explicit anti-aliasing filter, and it is able to handle wider-band signals with less power consumption in comparison with a discrete-time Delta-Sigma ADC. However, it inherently needs to sample the signal with a high-speed clock, necessitating a complex decimation filter that operates at high speed in order to convert the modulator output to a low-rate high-resolution digital signals without causing aliasing. This paper proposes a continuous-time Delta-Sigma ADC architecture that employs pulse-width modulation and shows that the proposed architecture lends itself to a simpler implementation of the decimation filter using a lookup table.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.19-26
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• 2007

In the sigma filter, there is a specialized neighbours distribution scheme in which the sigma value is computed from local statistics. It is designed to modify a standard average filter to preserve edges. However this filter is vulnerable to details-enhancement and conventional sigma approaches have been focused on denoising, not enhancing the characteristic area. This paper proposes an adaptive image enhancement algorithm using local statistics and functional synthesis which are utilized for adaptive realization of the enhancement, so that not only image noise may be smoothed but also details may be enhanced. For the local adaptation, parameters are estimated and weighted at each moving window that satisfy the criteria. The experimental results illuminates the effectiveness of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.4
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• pp.826-832
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• 2007

A recurrent neural network has been frequently used in equalizing the channel for fast communication systems. The existing techniques, however, have mainly dealt with time-invariant chamois. The modern environments of communication systems such as mobile ones have the time-varying feature due to fading. In this paper, powerful decision feedback - recurrent neural network is used as channel equalizer for nonlinear and time-varying system, and two kinds of algorithms, such as extended Kalman filter (EKF) and sigma-point Kalman filter (SPKF), are proposed; EKF is for fast convergence and good tracing function, and SPKF for overcoming the problems which can be developed during the process of first linearization for nonlinear system EKF.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1127-1139
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• 2013

In the realization of obtained image by various visual equipments, the addition of noise to the original image is a common phenomenon and the occurrence of the noise is practically impossible to prevent completely. Thus, the noise detection and reduction is an important foundational purpose. In this study, we detect the orientation about feature of images and estimate the level of noise variance based on the measurement of the relative proportion of the noise. Also, we apply the estimated level of noise to the sigma filter on noise reduction algorithm. And using the orientation about feature of images by weighted value, we propose the effective algorithm to eliminate noise. As a result, the proposed statistical noise reduction methodology provides significantly improved results over the usual sigma filtering and regardless of the estimated level of the noise variance.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.905-914
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• 2011

In the field of image processing, the removal noise contamination from the original image is essential. However, due to various reasons, the occurrence of the noise is practically impossible to prevent completely. Thus, the reduction of the noise contained in images remains important. In this study, we estimate the level of noise variance based on the measurement of the relative strength of the noise, and we propose a noise reduction algorithm that uses a sigma filter. As a result, the proposed statistical noise reduction methodology provides significantly improved results over the usual sigma filtering regardless of the level of the noise variance.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.869-878
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• 2009

Image restoration techniques such as noise reduction and contrast enhancement have been researched for enhancing a contaminated image by the noise. An image degraded by additive random noise can be enhanced by noise reduction. Sigma filtering is one of the most widely used method to reduce the noise. In this paper, we propose a new sigma filter algorithm based on noise variance estimation which effectively enhances the degraded image by noise. Specifically, the Bartlett test is used to measure the degree of noise with respect to the degree of image feature. Simulation results are also given to show the performance of the proposed algorithm.