• ETRI Journal
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• v.28 no.1
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• pp.51-58
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• 2006

In this paper, we propose a method of applying a lifting-based wavelet domain e-median filter (LBWDEMF) for image restoration. LBWDEMF helps in reducing the number of computations. An e-median filter is a type of modified median filter that processes each pixel of the output of a standard median filter in a binary manner, keeping the output of the median filter unchanged or replacing it with the original pixel value. Binary decision-making is controlled by comparing the absolute difference of the median filter output and the original image to a preset threshold. In addition, the advantage of LBWDEMF is that probabilities of encountering root images are spread over sub-band images, and therefore the e-median filter is unlikely to encounter root images at an early stage of iterations and generates a better result as iteration increases. The proposed method transforms an image into the wavelet domain using lifting-based wavelet filters, then applies an e-median filter in the wavelet domain, transforms the result into the spatial domain, and finally goes through one spatial domain e-median filter to produce the final restored image. Moreover, in order to validate the effectiveness of the proposed method we compare the result obtained using the proposed method to those using a spatial domain median filter (SDMF), spatial domain e-median filter (SDEMF), and wavelet thresholding method. Experimental results show that the proposed method is superior to SDMF, SDEMF, and wavelet thresholding in terms of image restoration.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1383-1391
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• 2018

A new nonlinear filtering algorithm for effectively denoising images corrupted by the random-valued impulse noise, called dual sliding statistics switching median (DSSSM) filter is presented in this paper. The proposed DSSSM filter is made up of two subunits; i.e. Impulse noise detection and noise filtering. Initially, the impulse noise detection stage of DSSSM algorithm begins by processing the statistics of a localized detection window in sorted order and non-sorted order, simultaneously. Next, the median of absolute difference (MAD) obtained from both sorted statistics and non-sorted statistics will be further processed in order to classify any possible noise pixels. Subsequently, the filtering stage will replace the detected noise pixels with the estimated median value of the surrounding pixels. In addition, fuzzy based local information is used in the filtering stage to help the filter preserves the edges and details. Extensive simulations results conducted on gray scale images indicate that the DSSSM filter performs significantly better than a number of well-known impulse noise filters existing in literature in terms of noise suppression and detail preservation; with as much as 30% impulse noise corruption rate. Finally, this DSSSM filter is algorithmically simple and suitable to be implemented for electronic imaging products.

• Science of Emotion and Sensibility
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• v.25 no.1
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• pp.67-78
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• 2022

Interest in bio-signal monitoring of wearable devices is increasing significantly as the next generation needs to develop new devices to dominate the global market of the information and communication technology industry. Accordingly, this research developed a resistive textile strain sensor through a wetting process in a single-wall carbon nanotube dispersion solution using an E-Band with low hysteresis. To measure the resistance signal in the E-Band to which electrical conductivity is applied, a universal material tester, an Arduino, and LCR meters that are microcontroller units were used to measure the resistance change according to the tensile change. To effectively handle various noises generated due to the characteristics of the fabric textile strain sensor, the filter performance of the sensor was evaluated using the moving average filter, Savitsky-Golay filter, and intermediate filters of signal processing. As a result, the reliability of the filtering result of the moving average filter was at least 89.82% with a maximum of 97.87%, and moving average filtering was suitable as the noise filtering method of the textile strain sensor.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.7
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• pp.1696-1705
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• 1996

The theory of stack filtering, which is a generalization of median filtering, is used to the detection of intensity edges in noisey images. The proposed approach, called the Difference of Estimates(DoE) approach, is a new formulation of a morphological scheme which has been very sensitive to impulse noise. In this approach, stack filters are applied to a noisy image to obtain local estimates of the dilated and eroded versions of the noise-free image. Thresholding the difference between these two estimates yields the binary edge map. We find that this approach yields results comparable to those obtained with the Canny operator for images with additive Gaussian noise, burt works much better when the noise is impulsive.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.3
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• pp.106-115
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• 1999

본 연구에서는 충격잡음이 부가되는 비정상 신호 및 잡음 환경하에서 실시간 시지연 추정이 가능한 SLMPTDE와 ZFLMSTDE의 새로운 적응 시지연 추정 방법을 제안하였다. 본 연구에서 제안한 방법은 중간직교 척도를 바탕으로 임의의 SαS 확률과정에 강건하게 적용할 수 있도록 유도된 확률적 경사형적응 추정 알고리즘으로 구성되었으며, SαS 분포를 갖는 다양한 충격잡음을 대상으로 모의 실험하여 알고리즘의 통계적 수렴특성 및 우정 오차에 대해 분석하였으며, 기존의 LMSTDE 방법과 일정시지연의 경우와 시변시지연의 경우에 대해 실시간 시지연 추정능력을 비교, 분석하였다. 실험결과로부터, LMSTDE 방법은 α≥1.9인 가우시안 잡음에 대해서만 시지연 추정이 가능하였고 P=1로 설정한 SLMPTDE 방법은 1〈α≤2인 경우의 SαS 잡음에 대해 정확한 시지연 추정능력을 보였으며, ZFLMSTDE 방법은 0〈α≤2인 모든 경우의 잡음 환경에 대해 그 능력이 입증되었다.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1228-1231
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• 2002

Digital image watermarking scheme using adaptive block division is proposed. To increase the perceptual invisibility, the image is divided into blocks by local properties and the human visual system (HVS), then the significant blocks are selected in the divided blocks. The significant coefficient is determined by Weber's law in these blocks. To increase the robustness, low frequency domains of the discrete cosine transform (DCT) and the discrete wavelet transform (DWT) are used. The watermark is embedded into the selected significant blocks of the DCT's and DWT's low frequency domains with adaptive watermark strengths. The watermark strength is determined by the variance and the local properties of the significant block. The experimental results prove that the proposed scheme has a good robustness against several image processing operations (e.g. median filtering, cropping, scaling, JPEG, JPEG2000, etc.) without significant degradation of the watermarked image.