• Journal of IKEEE
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• v.26 no.2
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• pp.240-248
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• 2022

Floating-point images that express pixel information as real numbers are used in HDR images. There have been various researches on efficient median filter algorithms, but most of them are applicable to 8-bit depth images and there are only a few number of algorithms applicable to floating-point images, including Gil and Werman's algorithm. In this paper, we propose a median filter algorithm that works efficiently on floating-point images by improving Kim's algorithm, which improved Gil and Werman's algorithm. Experimental results show that the execution time is improved by about 10% compared to the Kim's algorithm by reducing the redundant work for the repetitively used binary search tree and applying the inverted index.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.989-995
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• 2013

Image degradation caused by the impulse noise is generated in the process of image transmission and so on. It has been studied by many researchers in order to remove these noise. The representative impulse noise removal method includes SM filter. Though SM filter will indicate errors by the increasing of impulse noise density. Therefore, in this paper, in order to preserve the edges of the image, and reduce the distortion of the image, an improved adaptive median filter algorithm is proposed. In the simulation results, the algorithm showed excellent results in all several areas, and the PSNR is used as the criterion of evaluation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.10
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• pp.468-476
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• 2002

Digital images are easily corrupted by noise during the data transmission, data capture and data processing. A technical method of noise analyzing and adaptive filtering for reducing of quantum noise in medical radio-fluoroscopy images is presented. By adjusting the characteristics of the filter according to local statistics around each pixel of the image as moving windowing, it is possible to suppress noise sufficiently while preserve edge and other significant information required in diagnosis. We proposed adaptive weighed median(AWM) filters based on local statistics. We showed two ways of realizing the AWM filters. One is a simple type of AWM filter, which is constructed by Homogeneous factor(HF). Homogeneous factor(HF) from the noise models that enables the filter to recognize the local structures of the image is introduced, and an algorithm for determining the HF fitted to the diagnostic systems with various inner statistical properties is proposed. We show by the experimented that the performances of proposed method is superior to these of other filters and models in preserving small details and suppressing the noise at homogeneous region. The proposed algorithms were implemented by Visual C++ language on a IBM-PC Pentium 550 for testing purposes and the effects and results of the filter in the various levels of noise and images were proposed by comparing the values of NMSE(normalized mean square error) with the value of the other existing filtering methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.371-379
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• 2009

In this paper, we proposed new de-interlacing algorithm for video data using intensity gradient filter and median filter with texture detection in the image block. We first introduce the texture detection. According to texture detection, the current region is determined into smooth region or texture region. In case that the smooth region interpolated by median filter. In addition, in case of the texture region, we calculate missing pixel value using intensity gradient filter. Therefore, we analyze the local region feature using the texture detection and classify each missing pixel into two categories. And then, based on the classification result, a different de-interlacing algorithm is activated in order to obtain the best performance. Experimental results show that the proposed algorithm performs well with a variety of moving sequences compared with conventional intra-field method in the literature.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.1
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• pp.141-148
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• 1998

We propose an efficient method of de-interlacing in the post-pocessing of HDTV signal. Since the proposed method considers human visual weight, it gives the better performance in the edge information preservation than that of the median filter. The processing time of the proposed method is twice faster than median filter, therefore the proposed method is suitable for the real-time de-interlacing in the post-processing of HDTV signal.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.381-387
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• 2002

In this paper, an efficient method to find shot boundaries in the MPEG video stream data is proposed. For this purpose, we first assume that the histogram difference value(HDV) and pixel difference value(PDV) as an one dimensional signal and apply the median filter to these signals. The output of the median filter is subtracted from the original signal to produce the median filtered difference(MFD). The MFD is a criterion of shot boundary. In addition a neural network is employed and trained to find exactly cut boundary. The proposed algorithm shows that the cut boundaries are well extracted, especially in a dynamic video.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.83-88
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• 2005

In this paper, detection based - adaptive windowed nonlinear filters(DB-AWNF) are proposed for removing one-side impulse noise in infrared image. They are 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 $20\%$ and reserved the details of image. In quantitative view, PSNR was measured. The proposed could have 13 - 31[dB] more improved performance than those of median($3{\times}3$) filter and 18 - 25[dB] more improved performance than those of median($5{\times}5$) filter.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.188-190
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• 2012

Compressed Sensing (CS) has been developed for several years. Among many CS algorithms for image, the Block-based Compressed Sensing with Smoothed Projected Landweber (BCS-SPL) demonstrates its excellent performance in low-complexity and near-optimal reconstruction. Several noise filtering algorithms of image reconstruction have been introduced such as the Wiener or the median filters, etc. In general, each filter has its own advantages and disadvantages depending on specific coding scheme. In this paper, we show that reconstruction performance can be varied according to the choice of filter. When a sub-rate value is changed, different filter causes different effect as well. Concerning the sub-rate, an inner filter can be chosen to improve the reconstructed image quality.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.359-362
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• 2007

According to the development of information and communication technology, a great deal of researches have been taken in image digital processing field. And for earning the standard level of images, to remove noises such as impulse noise has became a essential demand. In those algorithms which remove impulse noise effectively, the standard median (SM) filter is representative. However, it has a disadvantage to degrade the quality of overall image by occurring errors in the edge domain. Therefore, in this paper, to restore the corrupted image by impulse noise, a nonlinear spatial filter based on Min-max operation was proposed and it was compared with conventional methods through simulations.

• Journal of the Korea Society of Computer and Information
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• v.4 no.2
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• pp.120-134
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• 1999

Image processing steps consist of image acquisition, pre-processing, region segmentation and recognition, and the images are easily corrupted by noise during the data transmission, data capture, and data processing. Impulse noise and gaussian noise are major noises, which can occur during the process. Many filters such as mean filter, median filter, weighted median filter, Cheikh filter, and Kyu-cheol Lee filter were proposed as spatial noise reduction filters so far. Many researches have been focused on the reduction of impulse noise, but comparatively the research in the reduction of gaussian noise has been neglected. For the reduction of gaussian noise, subset averaged median filter, using median information and subset average information of pixels in a window. was proposed. At this time, consider of the window size as 3$^{*}$3 pixel. The window is divided to 4 subsets consisted of 4 pixels. First of all, we calculate the average value of each subset, and then find the median value by sorting the average values and center pixel's value. In this paper, a better reduction of gaussian noise was proved. The proposed algorithms were implemented by ANSI C language on a Sun Ultra 2 for testing purposes and the effects and results of the filter in the various levels of noise and images were proposed by comparing the values of PSNR, MSE, and RMSE with the value of the other existing filtering methods.thods.