• Title/Summary/Keyword: Non-Local Mean Filtering

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Non-Local Mean based Post Processing Scheme for Performance Enhancement of Image Interpolation Method (이미지 보간기법의 성능 개선을 위한 비국부평균 기반의 후처리 기법)

  • Kim, Donghyung
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.16 no.3
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    • pp.49-58
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    • 2020
  • Image interpolation, a technology that converts low resolution images into high resolution images, has been widely used in various image processing fields such as CCTV, web-cam, and medical imaging. This technique is based on the fact that the statistical distributions of the white Gaussian noise and the difference between the interpolated image and the original image is similar to each other. The proposed algorithm is composed of three steps. In first, the interpolated image is derived by random image interpolation. In second, we derive weighting functions that are used to apply non-local mean filtering. In the final step, the prediction error is corrected by performing non-local mean filtering by applying the selected weighting function. It can be considered as a post-processing algorithm to further reduce the prediction error after applying an arbitrary image interpolation algorithm. Simulation results show that the proposed method yields reasonable performance.

Noise Removal in Magnetic Resonance Images based on Non-Local Means and Guided Image Filtering (비 지역적 평균과 유도 영상 필터링에 기반한 자기 공명 영상의 잡음 제거)

  • Mahmood, Muhammad Tariq;Choi, Young Kyu
    • KIISE Transactions on Computing Practices
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    • v.20 no.11
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    • pp.573-578
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    • 2014
  • In this letter, we propose a noise reduction method for use in magnetic resonance images that is based on non-local mean and guided image filters. Our method consists of two phases. In the first phase, the guidance image is obtained from a noisy image by using an adaptive non-local mean filter. The spread of the kernel is adaptively by controlled by implementing the concept of edgeness. In the second phase, the noisy images and the guidance images are provided to the guided image filter as input in order to produce a noise-free image. The improved performance of the proposed method is investigated by conducting experiments on standard datasets that contain magnetic resonance images. The results show that the proposed scheme is superior over the existing approaches.

Object-based Conversion of 2D Image to 3D (객체 기반 3D 업체 영상 변환 기법)

  • Lee, Wang-Ro;Kang, Keun-Ho;Yoo, Ji-Sang
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.36 no.9C
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    • pp.555-563
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    • 2011
  • In this paper, we propose an object based 2D image to 3D conversion algorithm by using motion estimation, color labeling and non-local mean filtering methods. In the proposed algorithm, we first extract the motion vector of each object by estimating the motion between frames and then segment a given image frame with color labeling method. Then, combining the results of motion estimation and color labeling, we extract object regions and assign an exact depth value to each object to generate the right image. While generating the right image, occlusion regions occur but they are effectively recovered by using non-local mean filter. Through the experimental results, it is shown that the proposed algorithm performs much better than conventional conversion scheme by removing the eye fatigue effectively.

Enhancing Medical Images by New Fuzzy Membership Function Median Based Noise Detection and Filtering Technique

  • Elaiyaraja, G.;Kumaratharan, N.
    • Journal of Electrical Engineering and Technology
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    • v.10 no.5
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    • pp.2197-2204
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    • 2015
  • In recent years, medical image diagnosis has growing significant momentous in the medicinal field. Brain and lung image of patient are distorted with salt and pepper noise is caused by moving the head and chest during scanning process of patients. Reconstruction of these images is a most significant field of diagnostic evaluation and is produced clearly through techniques such as linear or non-linear filtering. However, restored images are produced with smaller amount of noise reduction in the presence of huge magnitude of salt and pepper noises. To eliminate the high density of salt and pepper noises from the reproduction of images, a new efficient fuzzy based median filtering algorithm with a moderate elapsed time is proposed in this paper. Reproduction image results show enhanced performance for the proposed algorithm over other available noise reduction filtering techniques in terms of peak signal -to -noise ratio (PSNR), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), image enhancement factor (IMF) and structural similarity (SSIM) value when tested on different medical images like magnetic resonance imaging (MRI) and computer tomography (CT) scan brain image and CT scan lung image. The introduced algorithm is switching filter that recognize the noise pixels and then corrects them by using median filter with fuzzy two-sided π- membership function for extracting the local information.

Adaptive Switching Filtering Algorithm for SAP noise (SAP 잡음 제거를 위한 적응적 스위칭 필터링 알고리즘)

  • Kim, Donghyung
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.18 no.1
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    • pp.25-35
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    • 2022
  • The SAP(salt-and-pepper) noise changes the pixel value to the maximum and minimum values of the dynamic region of the pixel. For this reason, unlike white Gaussian noise, SAP noise can predict the ratio of noise relatively easily. Because the condition of the neighboring pixels that can be referenced changes according to the noise ratio, it is necessary to apply different noise reduction methods according to the noise ratio. This paper proposes an adaptive switching filtering algorithm which can eliminates the SAP noise. It consists of two phases. It first detects the location of the SAP noise and calculates the noise ratio. After that, the image is reconstructed using different methods depending on which of the three sections the calculated noise ratio belongs to. As a result of the experiment, the proposed method showed superior objective and subjective image quality compared to the previous methods such as MF, AFSWMF, NAMF and RWMF.

Local Adaptive Noise Cancellation for MCG Signals Based on Wavelet Transform (웨이브릿 변환을 기반으로 한 심자도 신호의 국소 적응잡음제거)

  • 김용주;박희준;원철호;이용호;김인선;김명남;조진호
    • Progress in Superconductivity
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    • v.5 no.1
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    • pp.26-30
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    • 2003
  • Magneto-cardiogram(MCG) signals may be highly distorted by the environmental noise, such as power-line interference, broadband white noise, surrounding magnetic noise, and baseline wondering. Several kinds of digital filters and noise cancellation methods have been designed and realized by many researchers, but these methods gave some problems that the original signal may be distorted by digital filter due to the wideband characteristics of background noise. To eliminate noise effectively without distortion of MCG signals, we performed multi-level frequency decomposition using wavelet packets and local adaptive noise cancellation in each local frequency range. In addition to the proposed wavelet filter to eliminate these various non-stationary noise elements, the local adaptive filter using the least mean square(LMS) algorithm and the soft threshold do-noising method are introduced in this paper. The signal to noise ratio(SNR) and the reconstruction square error(RSE) are calculated to evaluate the performance of the proposed method and compared with the results of the conventional wavelet filter and adaptive filter. The experimental results show that the proposed local adaptive filtering method is better than the conventional methods.

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Enhanced Block Matching Scheme for Denoising Images Based on Bit-Plane Decomposition of Images (영상의 이진화평면 분해에 기반한 확장된 블록매칭 잡음제거)

  • Pok, Gouchol
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.12 no.3
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    • pp.321-326
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    • 2019
  • Image denoising methods based on block matching are founded on the experimental observations that neighboring patches or blocks in images retain similar features with each other, and have been proved to show superior performance in denoising different kinds of noise. The methods, however, take into account only neighboring blocks in searching for similar blocks, and ignore the characteristic features of the reference block itself. Consequently, denoising performance is negatively affected when outliers of the Gaussian distribution are included in the reference block which is to be denoised. In this paper, we propose an expanded block matching method in which noisy images are first decomposed into a number of bit-planes, then the range of true signals are estimated based on the distribution of pixels on the bit-planes, and finally outliers are replaced by the neighboring pixels belonging to the estimated range. In this way, the advantages of the conventional Gaussian filter can be added to the blocking matching method. We tested the proposed method through extensive experiments with well known test-bed images, and observed that performance gain can be achieved by the proposed method.