• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.55 no.9
• /
• pp.429-434
• /
• 2006

In this paper, detection based - adaptive windowed nonlinear filter(DB-AWNF) is proposed for removing salt-pepper noise in infrared image. This filter is composed of impulse detector and window-size-variable median filters. Impulse detector checks whether current pixel is impulse or not using range function and nonlinear location estimator. If impulse is detected, current pixel is filtered according to four kinds of local masks by use of median filter. If not, current pixel is delivered to output like identity filter. In Qualitative view, the proposed could have removed heavy corrupted noise up to 30% and reserved the details of image. In quantitative view, PSNR was measured. The proposed could have about 12-31[dB] more improved performance than those of median $(3{\times}3)$ filter and 13-29[dB] more improved performance than those of median $(5{\times}5)$ filter.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.10a
• /
• pp.379-381
• /
• 2011

The image signal is often affected by the existence of noise, noise can occur during image capture, transmission or processing phases. noises caused the degradation phenomenon and demage the original signal information. Many studies are being accomplished to restore those signals which corrupted by mixed noise. In this paper, we proposed mixed weighted filter for removing Gaussian and impulse noise. we first charge the noise type, then, Gaussian is removed by a weighted mean filter and impulse noise is removed by self-adaptive weighted median filter that can not only remove mixed noise but also preserve the details. And through the simulation, we compared with the conventional algorithms and indicated that proposed method significant improvement over many other existing algorithms and can preserve image details efficiently.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.10
• /
• pp.2830-2837
• /
• 1999

Image processing steps are consist of image acquisition, preprocessing, region, segmentation and recognition. But image corrupted commonly by noise reduction methods, many filters were proposed like mean filter, median filter, weighted median filter, Cheikh filter, and Kyu-cheol lee filter as spatial noise reduction filtering. We propose a new edge detection algorithm so that we find out edge existence and nonexistence. In non-edge area, we selectively apply weighted median filter based upon using information of difference value between weighted median filter's value and center pixel's value. As a result, we finally prove a better performance of noise reduction by applying adaptive weighted median filter and improvement of processing time through using simple algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.393-396
• /
• 2010

In this paper the process of transmitting images signal restore to image corrupted by impulse noise proposed alpha-trimmed mean filter. the proposed filter first identifies the noise pixels using the morphological noise detector and then removes the detected impulse noise using the alpha-trimmed mean filter. these proposed filter can realize the accurate noise detection and it can remove impulse noise effectively while preserving edge region in the image very well. Through the simulation, we compared with the existing methods and capabilties.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.11 no.3
• /
• pp.471-478
• /
• 2007

The SA fillers encompass a large class of filters based on order statistics as veil as linear FIR filters. Using SA later structure, it is possible to design linear and non-linear filters under a unified framework. In this paper SA filters are applied to an image smoothing problem for mixed noise. Original image is contaminated by Gaussian and impulsive noise. Optimal SA filters are designed and applied to contaminated image. The experimental result shows that SA filters outperform linear FIR and ordering-based nonlinear filters.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.26 no.3
• /
• pp.156-184
• /
• 2022

In this article, we propose a novel variational model for restoring color images corrupted by mixed multiplicative Gamma noise and additive Gaussian noise. The model involves a data-fidelity term that characterizes the mixed noise as an infimal convolution of two noise distributions and the saturation-value total variation (SVTV) regularization. The data-fidelity term facilitates suitable separation of the multiplicative Gamma and Gaussian noise components, promoting simultaneous elimination of the mixed noise. Furthermore, the SVTV regularization enables adequate denoising of homogeneous regions, while maintaining edges and details and diminishing the color artifacts induced by noise. To solve the proposed nonconvex model, we exploit an alternating minimization approach, and then the alternating direction method of multipliers is adopted for solving subproblems. This contributes to an efficient iterative algorithm. The experimental results demonstrate the superior performance of the proposed model compared to other existing or related models, with regard to visual inspection and image quality measurements.

• Proceedings of the Korean Information Science Society Conference
• /
• 2002.04b
• /
• pp.670-672
• /
• 2002

본 논문에서는 잘못된 인쇄로 인한 문서상의 잡영이나 문자 훼손이 있는 문서를 복원 하고자 만다. 제안하는 방법은 스캐너로 읽어들인 문서영상을 잡영 제거론 만 다음 훼손된 숫자 영상에 대해서 프로젝션을 이용하여 숫자 열을 낱낱의 숫자로 분할한다. 각각의 숫자에 대해서 크기가 일정하도록 정규화를 시킨 다음, Backpropagalion을 이용하여 훼손된 숫자를 학습하였다. 학습시킨 다음 원 영상과 훼손된 영상을 각 픽셀단위로 비교하여 4-방향 마스크를 이용하여 원래의 숫자 영상으로 복원하도록 만다.

• Journal of Korea Multimedia Society
• /
• v.23 no.11
• /
• pp.1349-1360
• /
• 2020

In recent years, Convolutional Neural Networks (CNNs) have been successfully implemented in different tasks of computer vision. Since CNN models are the representatives of supervised learning algorithms, they demand large amount of data in order to train the classifiers. Thus, obtaining data with correct labels is imperative to attain the state-of-the-art performance of the CNN models. However, labelling datasets is quite tedious and expensive process, therefore real-life datasets often exhibit incorrect labels. Although the issue of poorly labelled datasets has been studied before, we have noticed that the methods are very complex and hard to reproduce. Therefore, in this research work, we propose Deep CleanNet - a considerably simple system that achieves competitive results when compared to the existing methods. We use K-means clustering algorithm for selecting data with correct labels and train the new dataset using a deep CNN model. The technique achieves competitive results in both training and validation stages. We conducted experiments using MNIST database of handwritten digits with 50% corrupted labels and achieved up to 10 and 20% increase in training and validation sets accuracy scores, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.4
• /
• pp.639-644
• /
• 2002

Nonlinear filters which are utilized rank-order information and temporal-order information, have many proposed, in order to restore nonstationary signals which are corrupted by additive noise. In this paper, we propose a data-dependent LWOS filter whose coefficients change based on local statistics. LWOS(Linear Combination of Weighted Order Statistics) filters［1］which also utilized two informations, and have properties of efficient impulsive and nonimpulsive noise attenuation and sufficiently details and edges preservation. DD-LWOS filters can remove non-impulsive oises while preserving signal details. DD-LWOS2 filter gets more better performance than DD-LWOS filter when input image corrupted by additive noise which includes Impulsive noise components.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.5
• /
• pp.1177-1182
• /
• 2011

The image signals are corrupted by various noises in signal processing and the noises caused the degradation phenomenon. gaussian noise occurs in the process of transmission. Many studies are being accomplished to restore those signals which corrupted by additive gaussian noise. In this paper, the algorithm is proposed to remove AWGN. The algorithm first calculates the mask's standard deviation and next according to the thresholds separated as three levels, then calculates the weight which for different location in the mask's pixels. At last the mean value of the modified mean filter's is the output. Also we compare existing methods through the simulation and using PSNR as the standard of judgement of improvement effect.