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Due to the widespread use of mobile devices and digital cameras, the usage of image data on various online platforms and social media has been rapidly increasing. However, during the data processing, digital images can exhibit various types of noise, with Additive White Gaussian Noise being one of the most common types. Various types of filters have been studied to remove such noise, but traditional methods have shown inadequate performance in either low-frequency or high-frequency regions. Therefore, this paper proposes an AWGN removal algorithm based on pixel distribution and spatial weighting. The algorithm utilizes absolute deviation values to analyze pixel data distribution, and based on this analysis, it separates each local mask into low-frequency and high-frequency regions. Additionally, it adaptively applies the algorithm by considering the pixel distribution in each region to effectively remove noise. Simulation results demonstrated that the proposed algorithm effectively preserved detailed information in high-frequency regions while also exhibiting excellent noise removal performance in low-frequency regions. [ABSTRACT FROM AUTHOR]

In recent years, digital imaging has played an essential role in medical diagnostics, security and surveillance, robotics engineering, and many other fields. However, noise can occur due to environmental factors or during data transmission, which degrades the image quality and causes information loss. AWGN(additive white gaussian noise) is a typical noise in digital images, and various filters have been studied to remove it. However, the existing methods show somewhat inadequate denoising performance by blurring the image while removing noise. Therefore, in this paper, we propose an AWGN denoising algorithm using a Gaussian weighted filter based on GMM(gaussian mixture model) clustering. The proposed algorithm simultaneously applies a mask for the probability of each pixel belonging to a cluster and a mask that follows a Gaussian distribution in space to improve the denoising performance while preserving the details in the high-frequency region. [ABSTRACT FROM AUTHOR]

With advances in digital image technology, researchers of various fields have developed image processing technologies. However, noise can affect image quality significantly in visual monitoring equipment or special devices —such as CMOS sensors, CT, MRI, and SEM—that are inevitably affected by the surrounding environment. AWGN generated in images is a noise that appears in all frequency bands and is characterized by the additive noise function that follows a Gaussian distribution. Many studies have been proposed in the past to remove AWGN. However, they could not improve image quality sufficiently and had poor edge preservation performance due to severe blurring. Therefore, this study proposes an algorithm that uses spatial and adaptive weights to restore damaged images effectively. The proposed filter uses a noise density weight and a median weight as spatial weights to remove surrounding pixels that have been severely damaged. In addition, it performs adaptive weight filtering on the center pixel to restore noisy images in detail. [ABSTRACT FROM AUTHOR]

As research on IoUT using underwater communication is actively progressing worldwide, in this study, research was conducted to increase the transmission rate by utilizing LFM (Linear Frequency Modulation) signals using chirp signals, one of the data transmission techniques. In this paper, we propose a system that combines the corresponding LFM signal into Up-LFM, Down-LFM, and Up&Down-LFM. The proposed system transmits and receives multiple bits in one chirp signal. The simulated channel environment is an AWGN (Additive White Gaussian Noise) channel, and the first simulation result showed that the performance of Up&Down-LFM was superior by about 2.1dB on average compared to Up-LFM and Down-LFM, and BPSK (Binary Phase Shift Keying), the performance comparison result with QPSK (Quadrature Phase Shift Keying) was excellent by about 5.86dB on average. The second simulation result confirmed a performance difference of about 5dB between Tx 1 bits and 5 bits at frequency sampling 50kHz, about 4dB at 100kHz, and 5dB at 200kHz. [ABSTRACT FROM AUTHOR]

In today’s society, automation and artificial intelligence technology are used in various fields, and image data is acquired using cameras and sensors to recognize or classify objects. However, noise can be caused by sensor defects or external signals in the process of acquiring image data, and noise removal is used in the preprocessing process because images damaged by noise adversely affect the system. This study proposes a filter algorithm using composite weights to minimize blurring that occurs in the process of removing AWGN. The proposed algorithm compares two regions using a central mask and a matching mask, and obtains weight by structural features, weight by pixel value comparison, and weight by pixel distribution, respectively. The output of the filter was calculated by convolutioning three weights to the pixel values of the matching area. Simulations were conducted to analyze the noise removal performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Recently, with the improvement of the performance of IoT technology and AI, automation and unmanned work are progressing in a wide range of fields, and interest in image processing, which is the basis of automation such as object recognition and object classification, is increasing. Image noise removal is an important process used as a preprocessing step in an image processing system, and various studies have been conducted. However, in most cases, it is difficult to preserve detailed information due to the smoothing effect in high-frequency components such as edges. In this paper, we propose an algorithm to restore damaged images in AWGN(additive white Gaussian noise) using fuzzy weights based on Gaussian distribution. The proposed algorithm switched the filtering process by comparing the filtering mask and the noise estimate with each other, and reconstructed the image by calculating the fuzzy weights according to the low-frequency and high-frequency components of the image. [ABSTRACT FROM AUTHOR]

Recently, with the development of artificial intelligence and IoT technology, the importance of video processing such as object tracking, medical imaging, and object recognition is increasing. In particular, the noise reduction technology used in the preprocessing process demands the ability to effectively remove noise and maintain detailed features as the importance of system images increases. In this paper, we provide a modified weight filter based on pixel matching in an AWGN environment. The proposed algorithm uses a pixel matching method to maintain high-frequency components in which the pixel value of the image changes significantly, detects areas with highly relevant patterns in the peripheral area, and matches pixels required for output calculation. Classify the values. The final output is obtained by calculating the weight according to the similarity and spatial distance between the matching pixels with the center pixel in order to consider the edge component in the filtering process. [ABSTRACT FROM AUTHOR]

Recently, with the development of artificial intelligence and IoT technology, automation, and unmanned work are progressing in various fields, and the importance of image processing, which is the basis of AI object recognition, is increasing. In particular, in systems that require detailed data processing, noise removal is used as a preprocessing step, but the existing algorithm does not consider the noise level of the image, so it has the disadvantage of blurring in the filtering process. Therefore, in this paper, we propose a modified Gaussian filter that determines the weight by determining the noise level of the image. The proposed algorithm obtains the noise estimate for the AWGN of the image using quadtree segmentation, determines the Gaussian weight and the pixel weight, and obtains the final output by convolution with the local mask. To evaluate the proposed algorithm, it was simulated compared to the existing method, and superior performance was confirmed compared to the existing method. [ABSTRACT FROM AUTHOR]

In modern society, various digital communication equipment is being used due to the influence of the 4th industrial revolution. Accordingly, interest in removing noise generated in a data transmission process is increasing, and research is being conducted to efficiently reconstruct an image. In this paper, we propose a filtering algorithm to remove the AWGN generated in the digital image transmission process. The proposed algorithm classifies pixels with high similarity by selecting regions with similar patterns around the input pixels according to block matching to remove the AWGN that appears strongly in the image. The selected pixel determines the estimated value by applying the weight obtained by the local steering kernel, and obtains the final output by adding or subtracting the input pixel value according to the standard deviation of the center mask. In order to evaluate the proposed algorithm, it was simulated with existing AWGN removal algorithms, and comparative analysis was performed using enlarged images and PSNR. [ABSTRACT FROM AUTHOR]

In modern society, various digital communication equipments are being used due to the influence of the 4th industrial revolution, and accordingly, interest in removing noise generated in the data transmission process is increasing. In this paper, we propose a filtering algorithm to remove AWGN generated during digital image transmission. The proposed algorithm removes noise based on mask matching to preserve information such as the boundary of an image, and uses pixel values with similar patterns according to the pattern of the input pixel value and the surrounding pixels for output calculation. To evaluate the proposed algorithm, we simulated with existing AWGN removal algorithms, and analyzed using enlarged image and PSNR comparison. The proposed algorithm has superior AWGN removal performance compared to the existing method, and is particularly effective in images with strong noise intensity of AWGN. [ABSTRACT FROM AUTHOR]

With the development of IoT technology, various digital equipment is being spread, and accordingly, the importance of data processing is increasing. The importance of data processing is increasing as it greatly affects the reliability of equipment, and various studies are being conducted. In this paper, we propose an algorithm to remove AWGN according to the characteristics of the fuzzy membership function. The proposed algorithm calculates the estimated value according to the correlation between the value of the fuzzy membership function between the input image and the pixel value inside the filtering mask, and obtains the final output by adding or subtracting the output of the spatial weight filter. In order to evaluate the proposed algorithm, it was simulated with existing AWGN removal algorithms, and analyzed using difference image and PSNR comparison. The proposed algorithm minimizes the effect of noise, preserves the important characteristics of the image, and shows the performance of efficiently removing noise. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm to remove AWGN by considering the characteristics of noise present in the image. The proposed algorithm uses block matching to calculate the output, and calculates an estimate by determining the similarity between the center mask and the matching mask. The output of the filter is calculated by adding or subtracting the estimated value and the input pixel value, and weighting is given according to the standard deviation of the center mask and the noise constant to obtain the final output. In order to evaluate the proposed algorithm, the simulation was performed in comparison with the existing methods, and analyzed through the enlarged image and PSNR comparison. The proposed algorithm minimizes the effect of noise, preserves important characteristics of the image, and shows the performance of removing noise efficiently. [ABSTRACT FROM AUTHOR]

In modern society, a variety of video media have been widely spread in line with the fourth industrial revolution and the development of IoT technology; in accordance with this trend, numerous researches have been performed to remove noise generated in image and data communications. However, the conventional Additive White Gaussian Noise (AWGN) cancellation techniques are likely to induce a blurring phenomenon in the noise removal process, thus impairing the information of the image. In this study, we propose an algorithm for minimizing the loss of image information in the removal process of AWGN. The proposed algorithm can apply weights according to the characteristics of noise by predicting AWGN in the image, where the output is calculated based on adding and subtracting the outputs of the high pass filter and the low pass filter. Compared to the existing method, the noise reduction using the proposed algorithm exhibited less blurring issues and better noise reduction properties in the AWGN removal process. [ABSTRACT FROM AUTHOR]

As use of digital equipment in various fields is increasing importance of processing video and signals is rising as well. However, in the process of sending and receiving signals, noise occurs due to different reasons and this noise bring about a huge influence on final output of the system. This research suggests algorithm for effectively repairing video in consideration to characteristics of its noise in condition where impulse and AWGN noises are combined. This algorithm tries to preserve video features by considering inference to noise components and resolution of filtering mask. Depending on features of input resolution, standard value is set and similar resolutions is selected for noise removal. This algorithm showing simulation result had outstanding noise removal and is compared and analyzed with existing methods by using different ways such as PSNR. [ABSTRACT FROM AUTHOR]

Noise removal is a pre-requisite procedure in image processing, and various methods have been studied depending on the type of noise and the environment of the image. However, for image processing with high-frequency components, conventional additive white Gaussian noise (AWGN) removal techniques are rather lacking in performance because of the blurring phenomenon induced thereby. In this paper, we propose an algorithm to minimize the blurring in AWGN removal processes. The proposed algorithm sets the high-frequency and the low-frequency component filters, respectively, depending on the pixel properties in the mask, consequently calculating the output of each filter with the addition or subtraction of the input image to the reference. The final output image is obtained by adding the weighted data calculated using the standard deviations and the Gaussian distribution with the output of the two filters. The proposed algorithm shows improved AWGN removal performance compared to the existing method, which was verified by simulation. [ABSTRACT FROM AUTHOR]

The importance of communication and data processing is increasing with the advance of the Fourth Industrial Revolution. Hence, the importance of video and data processing technologies, which directly influence the accuracy and reliability of equipment, is also increasing. In this research report we propose an algorithm for calculating the final output by estimating the standard deviation and estimate required for removing AWGN while adapting to changes in the frequency factors of video. This algorithm calculates the final output by checking an estimated value against the effective pixel range, which is obtained from the standard deviation of mask factors. Subsequently, the weighted value is computed, taking into account the filter output. To evaluate the functionality of this algorithm, it is compared with the most-commonly used present method through simulation. The simulation results show that the important features of the image are preserved and efficient noise cancellation performance is demonstrated. [ABSTRACT FROM AUTHOR]

Recently, interests in the field of image processing have increased with the increased demand for digital imaging devices. However, digital images are damaged by the noise generated by various causes during image processing. Generally in digital imaging devices, noise such as AWGN, etc. is generated and performance and reliability are deteriorated, and various researches are in progress to remove it. Accordingly in the present paper, an algorithm to protect the high frequency components of the images and to efficiently remove AWGN is proposed. The proposed algorithm calculates the final output by adjusting estimate values applied with Gaussian mask and weight values applied according to the local mask distribution. And for performance evaluation of the proposed algorithm, simulations were performed with the existing methods, and the characteristics were compared through PSNR and the processed, enlarged images. [ABSTRACT FROM AUTHOR]

Currently, the demand for multimedia services is increasing with the rapid development of the digital age. Image data is corrupted by various noises and typical noise is mainly AWGN, salt and pepper noise and the complex noise that these two noises are mixed. Therefore, in this paper, the noise is processed by classifying AWGN and salt and pepper noise through noise judgment. In the case of AWGN, the outputs of spatial weighted filter and pixel change weighted filter are composed and processed, and the composite weights are applied differently according to the standard deviation of the local mask. In the case of salt and pepper noise, cubic spline interpolation and local histogram weighted filters are composed and processed. This study suggested the multiple image restoration filter algorithm which is processed by applying different composite weights according to the salt and pepper noise density of the local mask. [ABSTRACT FROM AUTHOR]

In the process of obtaining and transmitting image, there is degradation of image due to various noise, and there have been many active studies ongoing to remove the noise added on the images. This thesis has proposed a switching filter processing by the types of noise in order to remove the complex noise added on images. When the center value of local mask is damaged by AWGN based on the noise judgment, the threshold value is applied on standard deviation of local mask to process by applying different weight of weighted mask, and if the image is damaged by the salt and pepper noise, the local mask is subdivided into 4 directions, and processed by applying cubic spine interpolation in the direction with the least slat and pepper noise. Also, in order to evaluate the performance of proposed filter algorithm, the study conducted comparison among the existing methods and proposed filter using PSNR. [ABSTRACT FROM AUTHOR]

Digital image processing is widely used in various fields including the military, medical, image recognition system, robot and commercial sectors. But in the process of acquiring and transmitting digital images, noise is generated by various external causes. There are various types of general noise depending on the cause and form, but AWGN and impulse noise is one of the leading methods. Removing noise during image processing is essential to the pre-treatment process such as segmentation, image recognition and characteristic extraction. As such, this paper suggests an algorithm that distinguishes the non-edge area and edge area using the Canny edge to apply different filters to different areas in order to effectively remove noise from the image. To verify the effectiveness of the suggested algorithm, it was compared against existing methods using zoom images, edge images and PSNR(peak signal to noise ratio). [ABSTRACT FROM AUTHOR]

In an era where digital data takes on great importance, images are essential to various media. Noise is generated during the acquisition and transmission of such images, caused by a number of external factors. The removal of noise is an essential step in image processing. There are various methods used to remove noise, in accordance with the cause or form of the noise. AWGN is one of the leading methods. As such, this paper applies the edge detection method using the mean of each pixel after categorizing in detail the partial masks into nine areas as part of the preliminary process, in order to minimize noise that had been added to the image. In addition, the paper suggests an algorithm that applies different filters to the partial masks by using the critical mass value of the transfigured edge detection. To verify the competence of the suggested algorithm, it was compared with existing methods by using magnified images and PSNR(peak signal to noise ratio). [ABSTRACT FROM AUTHOR]

Recently, as demand of video processor unit rapidly increases, excellent quality of the video has been required. However, generally, video data occurs the quick flame of video due to various external causes in process of acquisition, treatment, and transmission, and major cause of the quick flame of the video is known as the noise. There are various kinds of noise, which are added to the video, AWGN is a typical one. Thus, this thesis suggested algorithm that treats in three methods by scale of the edge through using edge information of local masks. In case that edge pixel is big, it applied spatial weighting according to equation of straight line about direction of edge pixel. In case that edge pixel is middle, it suggested algorithm with spatial weighting filter and average filter, and for the smooth territory, it suggested algorithm that treats with average filter. [ABSTRACT FROM AUTHOR]

Recently, with the rapid demand expansion on the devices of digital image processing, the excellent quality of the images is required. However, the degradation of the images occurs in the processes of data acquisition, processing, and transmission by various external causes and the noise has been well known as the major cause of image degradation. There are a variety of noises to be added on the images, with typical example of AWGN. Hence, in this article, we suggested average filter algorithm processed by the threshold values using them applying standard deviation of local mask under the AWGN environment in this study. As the result of the simulation, The proposed algorithm shows a high PSNR of 24.56[dB] for Barbara images that had been damaged of AWGN(σ=15), compared to the existing MF, CWMF and AWMF there were improvements by 3.34[dB], 2.57[dB], and 3.32[dB], respectively. [ABSTRACT FROM AUTHOR]