• Title, Summary, Keyword: Denoising

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Non-Local Means Denoising Method using Weighting Function based on Mixed norm (혼합 norm 기반의 가중치 함수를 이용한 평균 노이즈 제거 기법)

  • Kim, Dong-Young;Oh, Jong-Geun;Hong, Min-Cheol
    • Journal of IKEEE
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    • v.20 no.2
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    • pp.136-142
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    • 2016
  • This paper presents a non-local means (NLM) denoising algorithm based on a new weighting function using a mixed norm. The fidelity of the difference between an anchor patch and the reference patch in the NLM denoising depends on noise level and local activity. This paper introduces a new weighting function based on a mixed norm type of which the order is determined by noise level and local activity of an anchor patch, so that the performance of the NLM denoising can be enhanced. Experimental results demonstrate the objective and subjective capability of the proposed algorithm. In addition, it was verified that the proposed algorithm can be used to improve the performance of the other $l_2$ norm based non-local means denoising algorithms

A Study on Hybrid Filter Algorithm for Image Denoising (영상 잡음제거를 위한 하이브리드 필터 알고리즘에 관한 연구)

  • Yinyu, Gao;Kim, Nam-Ho
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.127-129
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    • 2012
  • Due to the prevalence of digital camera, multi-media etc. the image is being used in everyday life. However, noise always damages the image and the image denoising technology is important part for improving the image visual quality. There are many existing methods to remove noise such as wiener filter, mean filter and VisuShrink etc. However, they perform not good enough for denoising. Hence, in this paper we proposed a hybrid filter algorithm which consists of wiener filter and modified wavelet based thresholding method using adaptive threshold and thresholding function. The proposed algorithm shows not only better low frequency and high frequency property, but also the outstanding noise suppression and edge preservation properties.

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POCS Based Interpolation Method for Irregularly Sampled Image (불규칙한 샘플 영상에 대한 POCS 기반 보간법)

  • Lee, Jong-Hwa;Lee, Chul-Hee
    • Journal of Broadcast Engineering
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    • v.16 no.4
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    • pp.669-679
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    • 2011
  • In this paper, we propose a POCS based irregularly sampled image interpolation method exploiting non-local block-based wavelet shrinkage denoising algorithm. The method provides convex sets to improve the performance. The Delaunay triangulation interpolation is first applied to interpolate the missing pixels of the irregularly sampled image into the regular grids. Then, the non-local block-based wavelet shrinkage denoising algorithm is applied, and the originally observed pixels are enforced. After iteration is performed, the denoising algorithm for non-edge areas is applied to acquire the final result. The experimental results show that the proposed method outperforms the conventional methods.

Image Denoising via Mixture Modeling of Wavelet Coefficients (웨이블릿 계수의 혼합 모델링을 이용한 영상 잡음 제거)

  • 엄일규;우동헌;김유신
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.28 no.8C
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    • pp.788-794
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    • 2003
  • It is very important to construct statistical model in order to exactly estimate the signal variance from the noisy image. By using estimated variance of original image, in general, Wiener filter is constructed, and it is applied to the noisy image. In this paper, we propose a new statistical mixture modeling of wavelet coefficients for image denoising. Firstly, a simple classification method is used to construct a significance map that captures significant property of wavelet coefficients. Based upon the significance map, the state probabilities of mixture model is computed, and signal variance is estimated by using them. Experimental results show that the proposed method yields 0.1-0.2㏈ higher PSNR than conventional methods for image denoising.

Noise Reduction Using Gaussian Mixture Model and Morphological Filter (가우스 혼합모델과 형태학적 필터를 이용한 잡음 제거)

  • Eom Il-Kyu;Kim Yoo-Shin
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.1
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    • pp.29-36
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    • 2004
  • Generally, wavelet coefficients can be classified into two categories: large coefficients with much signal information and small coefficients with little signal component. This statistical characteristic of wavelet coefficient is approximated to Gaussian mixture model and efficiently applied to noise reduction. In this paper, we propose an image denoising method using mixture modeling of wavelet coefficients. Binary mask value is generated by proper threshold which classifies wavelet coefficients into two categories. Information of binary mask value is used to remove image noise. We also develope an enhancement method of mask value using morphological filter, and apply it to image denoising for improvement of the proposed method. Simulation results shows the proposed method have better PSNRs than those of the state of art denoising methods.

Bayesian Image Denoising with Mixed Prior Using Hypothesis-Testing Problem (가설-검증 문제를 이용한 혼합 프라이어를 가지는 베이지안 영상 잡음 제거)

  • Eom Il-Kyu;Kim Yoo-Shin
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.43 no.3
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    • pp.34-42
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    • 2006
  • In general, almost information is stored in only a few wavelet coefficients. This sparse characteristic of wavelet coefficient can be modeled by the mixture of Gaussian probability density function and point mass at zero, and denoising for this prior model is peformed by using Bayesian estimation. In this paper, we propose a method of parameter estimation for denoising using hypothesis-testing problem. Hypothesis-testing problem is applied to variance of wavelet coefficient, and $X^2$-test is used. Simulation results show our method outperforms about 0.3dB higher PSNR(peak signal-to-noise ratio) gains compared to the states-of-art denoising methods when using orthogonal wavelets.

Modified Adaptive Gaussian Filter for Removal of Salt and Pepper Noise

  • Li, Zuoyong;Tang, Kezong;Cheng, Yong;Chen, Xiaobo;Zhou, Chongbo
    • 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.

High-frame-rate Video Denoising for Ultra-low Illumination

  • Tan, Xin;Liu, Yu;Zhang, Zheng;Zhang, Maojun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.8 no.11
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    • pp.4170-4188
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    • 2014
  • In this study, we present a denoising algorithm for high-frame-rate videos in an ultra-low illumination environment on the basis of Kalman filtering model and a new motion segmentation scheme. The Kalman filter removes temporal noise from signals by propagating error covariance statistics. Regarded as the process noise for imaging, motion is important in Kalman filtering. We propose a new motion estimation scheme that is suitable for serious noise. This scheme employs the small motion vector characteristic of high-frame-rate videos. Small changing patches are intentionally neglected because distinguishing details from large-scale noise is difficult and unimportant. Finally, a spatial bilateral filter is used to improve denoising capability in the motion area. Experiments are performed on videos with both synthetic and real noises. Results show that the proposed algorithm outperforms other state-of-the-art methods in both peak signal-to-noise ratio objective evaluation and visual quality.

The Effects of Image Dehazing Methods Using Dehazing Contrast-Enhancement Filters on Image Compression

  • Wang, Liping;Zhou, Xiao;Wang, Chengyou;Li, Weizhi
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.7
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    • pp.3245-3271
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    • 2016
  • To obtain well-dehazed images at the receiver while sustaining low bit rates in the transmission pipeline, this paper investigates the effects of image dehazing methods using dehazing contrast-enhancement filters on image compression for surveillance systems. At first, this paper proposes a novel image dehazing method by using a new method of calculating the transmission function—namely, the direct denoising method. Next, we deduce the dehazing effects of the direct denoising method and image dehazing method based on dark channel prior (DCP) on image compression in terms of ringing artifacts and blocking artifacts. It can be concluded that the direct denoising method performs better than the DCP method for decompressed (reconstructed) images. We also improve the direct denoising method to obtain more desirable dehazed images with higher contrast, using the saliency map as the guidance image to modify the transmission function. Finally, we adjust the parameters of dehazing contrast-enhancement filters to obtain a corresponding composite peak signal-to-noise ratio (CPSNR) and blind image quality assessment (BIQA) of the decompressed images. Experimental results show that different filters have different effects on image compression. Moreover, our proposed dehazing method can strike a balance between image dehazing and image compression.

ECG Denoising by Modeling Wavelet Sub-Band Coefficients using Kernel Density Estimation

  • Ardhapurkar, Shubhada;Manthalkar, Ramchandra;Gajre, Suhas
    • Journal of Information Processing Systems
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    • v.8 no.4
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    • pp.669-684
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    • 2012
  • Discrete wavelet transforms are extensively preferred in biomedical signal processing for denoising, feature extraction, and compression. This paper presents a new denoising method based on the modeling of discrete wavelet coefficients of ECG in selected sub-bands with Kernel density estimation. The modeling provides a statistical distribution of information and noise. A Gaussian kernel with bounded support is used for modeling sub-band coefficients and thresholds and is estimated by placing a sliding window on a normalized cumulative density function. We evaluated this approach on offline noisy ECG records from the Cardiovascular Research Centre of the University of Glasgow and on records from the MIT-BIH Arrythmia database. Results show that our proposed technique has a more reliable physical basis and provides improvement in the Signal-to-Noise Ratio (SNR) and Percentage RMS Difference (PRD). The morphological information of ECG signals is found to be unaffected after employing denoising. This is quantified by calculating the mean square error between the feature vectors of original and denoised signal. MSE values are less than 0.05 for most of the cases.