• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.207-209
• /
• 2021

In recent years, with the development of artificial intelligence and IoT technology, automation and unmanned technology are in progress in various fields, and the importance of image processing such as object tracking, medical images and object recognition, which are the basis of this, is increasing. In particular, in systems requiring detailed data processing, noise reduction is used as a pre-processing step, but the existing algorithm has a disadvantage that blurring occurs in the filtering process. Therefore, in this paper, we propose a filter algorithm using modified spatial weights to minimize information loss in the filtering process. The proposed algorithm uses mask matching to remove AWGN, and obtains the output of the filter by adding or subtracting the output of the modified spatial weight. The proposed algorithm has superior noise reduction characteristics compared to the existing method and reconstructs the image while minimizing the blurring phenomenon.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.3
• /
• pp.724-729
• /
• 2013

Recently, with the popularization of digital devices, the requirements of image quality is becoming higher and higher. However, the images are frequently corrupted in the image data processing, there are several reasons for this and the noise is considered as the main reason. Therefore, in order to alleviate the influence of AWGN(additive white Gaussian noise) in image, this paper puts forward the spatial weighted filtering algorithm. The algorithm set the weighted value according to the spatial distance, compared with the existing methods. The algorithm not only alleviated the influence of AWGN effectively but also reserved image details.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.5
• /
• pp.1227-1232
• /
• 2013

Recently, the expectation of quality about images over the increasing demand of digital devices is increasing with the development of the technology of the digital. But the images are degraded by a variety of causes, and the main reason is the noises. Therefore, the necessity of denoising comes to the fore, and the research for denoising is progressing dynamically. The images are mainly degraded by AWGN(additive white Gaussian noise), and the characteristics of denoising of existing methods such as mean filter are insufficient. In this paper, an algorithm combined by the spatial weighted filter and the modified adaptive weighted filter is proposed in order to effectively remove the AWGN. In the simulation result, the proposed algorithm showed excellent denoising capabilities.

• Journal of Broadcast Engineering
• /
• v.18 no.3
• /
• pp.455-462
• /
• 2013

This paper introduces the Spatially Preprocessed Speech Distortion Weighted Multi-channel Wiener Filter (SP-SDW-MWF) for multi-microphone noise reduction and proposes a method to determine the speech distortion weights. The SP-SDW-MWF is known as a robust noise reduction algorithm against the error caused by the mismatch in microphones. The SP-SDW-MWF adopts weights which determine the amount of noise reduction at the expense of introducing speech distortion in the noise-suppressed speech. In this paper, we use the error of power spectral density between the estimated signal and the desired signal as the evaluation measure. Thus the a priori SNR is used to control the speech distortion weights in the frequency domain. In the experimental results, the proposed method yields better result in terms of MFCC distortion compared to the conventional method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.3
• /
• pp.629-635
• /
• 2017

In this paper, we proposed a nonlinear synthesis filter for noise reduction to reduce the effects of Gaussian noise and impulse noise. When the centralization of the local mask is judged to be Gaussian noise by the noise judgment, the weight value of the weight filter are applied differently according to the spatial weight filter and the pixel change by using the sample variance in the local mask. And if it is determined as the impulse noise, we proposed an algorithm that applies different weights of local histogram weight filter and standard median filter according to noise density of mask. In order to evaluate the performance of the proposed filter algorithm, we used PSNR(peak signal to noise ratio) and compared existing methods and proposed filter algorithm in the mixed noise environment with Gaussian noise, impulsive noise, and two noises mixed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.210-212
• /
• 2021

In modern society, various digital equipment are being distributed due to the influence of the 4th industrial revolution, and they are used in a wide range of fields such as automated processes, intelligent CCTV, medical industry, robots, and drones. Accordingly, the importance of the preprocessing process in a system operating based on an image is increasing, and an algorithm for effectively reconstructing an image is drawing attention. In this paper, we propose a filter algorithm based on a combined weight value to reconstruct an image in a complex noise environment. The proposed algorithm calculates the weight according to the spatial distance and the weight according to the difference between the pixel values for the input image and the pixel values inside the filtering mask, respectively. The final output was filtered by applying the join weights calculated based on the two weights to the mask. In order to verify the performance of the proposed algorithm, we simulated it by comparing it with the existing filter algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.409-411
• /
• 2016

Salt and pepper noise is caused by various causes such as camera malfunction, storage media memory error, and transmission channel error. Representative filters to remove salt and pepper noise include SMF(standard median filter), CWMF(center weighted median filter), and AMF(adaptive median filter). However previous filters have inadequate noise removal characteristics in high density salt-and-pepper noise environment. Therefore the study suggested a composite filter which, through noise evaluation, preserves original pixels when the central pixel is non-noise, and uses spatial weighted value mask and median when there is noise.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.12 no.4
• /
• pp.76-86
• /
• 2013

As the basic Kalman filter is limited to be used for indoor navigation, and particle filters incur serious computational overhead, especially in mobile devices, we propose an adaptive hybrid filter for WiFi-based indoor positioning systems. The hybrid filter utilizes the same prediction framework of the basic Kalman filter, and it adopts the notion of particle filters only using a small number of particles. Restricting the predicts of a moving object to a small number of particles on a way network and substituting a dynamic weighting scheme for Kalman gain are the key features of the filter. The adaptive hybrid filter showed significantly better accuracy than the basic Kalman filter did, and it showed greatly improved performance in processing time and slightly better accuracy compared with a particle filter.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.213-215
• /
• 2021

With the development of IoT technology, various technologies such as artificial intelligence and automation are being grafted into industrial sites, and accordingly, the importance of data processing is increasing. In particular, a system based on a digital image may cause a malfunction due to noise in the image due to a sensor defect or a communication environment problem. Therefore, research on image processing has been continued as a pre-processing process, and an effective noise reduction technique is required depending on the type of noise and the characteristics of the image. In this paper, we propose a modified spatial weight filter to protect edge components in the impulse noise reduction process. The proposed algorithm divides the filtering mask into four regions and calculates the standard deviation of each region. The final output was filtered by applying a spatial weight to the region with the lowest standard deviation value. Simulation was conducted to evaluate the performance of the proposed algorithm, and it showed superior impulse noise reduction performance compared to the existing method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.05a
• /
• pp.623-625
• /
• 2012

The restoration of an image corrupted by Gaussian noise is an important task in image processing. There are many kinds of filters are proposed to remove Gaussian noise such as Gaussian filter, mean filter, weighted filter, etc. However, they perform not good enough for denoising and edge preservation. Hence, in this paper we proposed an adaptive weighted filter which considers spatial distance and the estimated variance of noise. We also compared the proposed method with existing methods through the simulation and used MSE(mean squared error) as the standard of judgement of improvement effect.