• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.591-596
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• 2011

Image denoising is a key issue in all image processing researches. Generally, the quality of an image could be corrupted by a lot of noise due to the undesired conditions of image acquisition phase or during the transmission. Many approaches to image restoration are aimed at removing either Gaussian or impulse noise. Nevertheless, it is possible to find them operating on the same image, which is called mixed noise and it produces a hard damage. In this paper, we proposed noise type classification method and a mixed nonlinear filter for mixed noise suppression. The proposed filtering scheme applies a modified adaptive switching median filter to impulse noise suppression and an efficient nonlinear filer was carried out to remove Gaussian noise. The simulation results based on Matlab show that the proposed method can remove mixed Gaussian and impulse noise efficiently and it can preserve the integrity of edge and keep the detailed information.

• Journal of information and communication convergence engineering
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• v.15 no.2
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• pp.131-136
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• 2017

Chest X-ray image cannot be focused in the same manner as optical lenses and the resultant image generally tends to be slightly blurred. Therefore, appropriate methods to improve the quality of chest X-ray image have been studied in this paper. As the frequency domain filters work well for slight blurring and moderate levels of additive noises, we propose an algorithm that is particularly suitable for enhancing chest image. First, the chest image using Gaussian high pass filter and the optimal high frequency emphasis filter shows improvements in the edges and contrast of the flat areas. Second, as compared to using histogram equalization where each pixel of chest image is characterized by a loss of detail and much noises, in using fuzzy logic, each pixel of chest image shows the detail preservation and little noise.

• Journal of Integrative Natural Science
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• v.8 no.2
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• pp.141-144
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• 2015

As the visual quality of X-ray images is a critical reference for the accuracy of the clinical diagnosis, the methods to improve the quality of X-ray images have been investigated. Among many existing methods, using frequency domain filter is a very powerful method to improve the visual quality of images. In this paper, the inherent noises of the input images are suppressed by adding the Laplacian image to the subjected image. The medical X-ray images using the optimal high pass filter has shown improved edges. Further, the optimal high frequency emphasis filter has shown the improved contrast of flat areas by using the result image from the optimal high pass filter. Also the resulting images of the global contrast have improved by the histogram equalization. As a result, the proposed methods have shown enhanced contrast and edges of the images with noise canceling effect.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.804-806
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• 2014

During the transmission process of processing of image, various noises are added causing an error to the image. In order to restore these images, methods such as alpha trimmed average filter, standard median filter and switching median filter were proposed but such previous methods has insufficient noise suppression characteristics. Therefore in this paper, in order to remove the impulse noise added to the image, an adaptive median filter algorithm modified to expand the mask according to the noise density of mask pixels was proposed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2928-2947
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• 2015

Adaptive Gaussian filter (AGF) is a recently developed switching filter to remove salt and pepper noise. AGF first directly identifies pixels of gray levels 0 and 255 as noise pixels, and then only restored noise pixels using a Gaussian filter with adaptive variance based on the estimated noise density. AGF usually achieves better denoising effect in comparison with other filters. However, AGF still fails to obtain good denoising effect on images with noise-free pixels of gray levels 0 and 255, due to its severe false alarm in its noise detection stage. To alleviate this issue, a modified version of AGF is proposed in this paper. Specifically, the proposed filter first performs noise detection via an image block based noise density estimation and sequential noise density guided rectification on the noise detection result of AGF. Then, a modified Gaussian filter with adaptive variance and window size is used to restore the detected noise pixels. The proposed filter has been extensively evaluated on two representative grayscale images and the Berkeley image dataset BSDS300 with 300 images. Experimental results showed that the proposed filter achieved better denoising effect over the state-of-the-art filters, especially on images with noise-free pixels of gray levels 0 and 255.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.223-227
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• 1996

In this paper we propose an adaptive fast image restoration filter, which is suitable for reducing the blocking artifacts in the compressed image in real-time. The proposed restoration filter is based on the observation that quantization operation in a series of coding process is a nonlinear and many-to-one mapping operator. And then we propose an approximated version of constrained optimization technique as a restoration process for removing the nonlinear and space varying degradation operator. We also propose a novel block classification method for adaptively choosing the direction of a highpass filter, which serves as a constraint in the optimization process. The proposed classification method adopts the bias-corrected maximized likelihood, which is used to determine the number of regions in the image for the unsupervised segmentation. The proposed restoration filter can be realized either in the discrete Fourier transform domain or in the spatial domain in the form of a truncated finite impulse response (FIR) filter structure for real-time processing. In order to demonstrate the validity of the proposed restoration filter experimental results will be shown.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.165-168
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• 2008

With the development of the information technology in recent years, the innovation of multimedia information technology also has been accelerated. Many fields of the digital image processing technologies as image data compression, recognition. restoration, etc. are now being studied actively. When transmitting and saving digital images, noise would be made, and we are using the image filters to remove the noise. The Image Filter used Digital Image Process basically has a two-dimensional structure. There are two methods of the filter's creation - One is reiterating one dimension, and the other is using an indivisible two-dimension image filter. The space domain method using mask is the latter one. That is inserting the value-multiplied pixels values faced each other when the two-dimension filter overlapped on input image- to the filter value's center position and the same position in the image. The image filter is being used widely along with one-dimension filter, according each noise. Most people are using various median filters to remove the impulse noise. However, in this paper, I suggested a powerful switching median filter and compared with conventional method for verification.

• Journal of Biomedical Engineering Research
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• v.13 no.1
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• pp.25-32
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• 1992

A new restoration method of chest X -ray image (dual project filter) was proposed to improve SNR(signal to noise ratio) characteristics. In this method, a priori Information of system and anatomical structure and statistics of projected object are used in the design of filter. Dual projection filter varies its parameters, adapting to the local regions of chest(lung region, mediasternum, subdiaphragm) and the structure of chest (bone, tissue, blood vessel, bronchia). The performance of Dual Projection Filter was 0.1-0.2dB better than Dual Sensor Wiener Filter, which was used for initial estimate of Dual Porjection Filter.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.5
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• pp.1141-1154
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• 1996

The para-unitary (PU) M band filter bank, which can beused for M band decomposition, has many useful properties. In this ppaer, attempts have been made to design and and analyze the linear phase para-unitary (LPPU) M band filter bank, which is appropriate to the image coding application. First, we derive a unified coding gain in terms of the correlation in the band, as well as the energy compaction. And M band filter bank has been designed, maximizing the new coding gain. Then, we analyze the image coding performance of the LPPU M band filter bank, such as the energy compaction, the correlation in the band and the entropy. From the analysis, it is shown that the coding gain for LPPU M band filter bank improves, and the coding gain for the LPPU 4 band filter bank approaches very closely to that for LPPU 8 band filter bank, as the length of the filter increases. This fact is also verified by the coding results on the real images.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.4
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• pp.611-617
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• 2004

Pixel classification is one of basic image processing issues. The general characteristics of the pixels belonging to various classes are discussed and the radical principles of pixel classification are given. At the same time. a pixel classification scheme based on image direction measure is proposed. As a typical application instance of pixel classification, an adaptive multi-level median filter is presented. An image can be classified into two types of areas by using the direction information measure, that is. smooth area and edge area. Single direction multi-level median filter is used in smooth area. and multi-direction multi-level median filter is taken in the other type of area. What's more. an adaptive mechanism is proposed to adjust the type of the filters and the size of filter window. As a result. we get a better trade-off between preserving details and noise filtering.