A Fuzzy Noise Reduction Method for Color Images

Schulte, Stefan; Witte, Valérie De; Kerre, Etienne

doi:10.1109/tip.2007.891807

Cited by 74 publications

(50 citation statements)

References 43 publications

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“…Add the Gaussian noise to the image. Perform image denoising via wavelet [13] and wiener filter and fuzzy logic with haar wavelet (ATMAV AND ATMED FILTER) [14]. Table shows …”

Section: Resultsmentioning

confidence: 99%

Performance Analysis of Image de-noising using Fuzzy and Wiener Filter in Wavelet Domain

Mehra¹,

Moraniya²,

Nitnawwre³

2014

IJCA

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Images are nowadays, very fundamental type data for transmission. Due to the various components and high speed transmission, images are corrupted by the noises. The Image denoising is required at the receiver end for the faithful communication. There are several methods for image denoising in spatial and transform domain. The current trends of the image denoising research are the evolution of mixed domain methods. In this paper, a mixed domain image denoising method is proposed, which is based on the wavelet transform, median filter and nonlinear diffusion based methods. The wavelet transform is used in this paper to convert the spatial domain image to wavelet domain coefficients. WT produces approximation, horizontal detail, vertical detail and diagonal detail coefficient which represent the various spatial frequency bands. The detail component are removed due to the most of the image part is in approximation part. The approximation coefficient is also filter by fuzzy filters and wiener filter separately. Median and moving average based fuzzy filter are used to apply the filtering on probabilistic way. The trapezoidal membership function are used for the filtering .The peak signal to noise ratio (PSNR) and mean square error (MSE) are used as the performance parameter. The Haar wavelet is used with various filters to optimize the performance of denoising. The combination of Haar wavelet and ATMED filter are giving the best denoising result.

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“…Add the Gaussian noise to the image. Perform image denoising via wavelet [13] and wiener filter and fuzzy logic with haar wavelet (ATMAV AND ATMED FILTER) [14]. Table shows …”

Section: Resultsmentioning

confidence: 99%

Performance Analysis of Image de-noising using Fuzzy and Wiener Filter in Wavelet Domain

Mehra¹,

Moraniya²,

Nitnawwre³

2014

IJCA

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show abstract

“…Then the wavelet coefficient is multiplied with the probability Schulte et al, 2007) introduced a fuzzy version of probabilistic shrinkage method. Its core is shrinkage based on local mean of wavelet coefficients and some fuzzy rules (Schulte et al, 2006).…”

Section: Jcsmentioning

confidence: 99%

Fuzzy Shrink Image Denoising Using Smoothing Spline Estimation

Rajathi¹,

Rangarajan²,

R³

et al. 2014

Journal of Computer Science

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The image data is normally corrupted by additive noise during acquisition. This reduces the accuracy and reliability of any automatic analysis. For this reason, denoising methods are often applied to restore the original image. In proposed method a wavelet shrinkage algorithm based on fuzzy logic and the DT-DWT scheme is used. In particular, intra-scale dependency within wavelet coefficients is modeled using a fuzzy feature. This model differentiates the important coefficients and the coefficients belong to image discontinuity and noisy coefficients. This fuzzy model is used to enhance the wavelet coefficients' information in the shrinkage step which uses the fuzzy membership function to shrink wavelet coefficients based on the fuzzy feature. The effectiveness of image denoising depends upon the estimation of noise variance of noisy image, the noise variance is estimated using smoothing spline Estimation. This study examine image denoising algorithm in the dual-tree discrete wavelet transform, which is the new shiftable and modified version of discrete wavelet transform. Simulation result shows our approach achieves a substantial improvement in both PSNR and Visual quality.

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“…In addition, mean-based filters [18][19][20] can be used as an alternative solution in reducing the high load computation. Recently, some methods [21][22][23][24] have been proposed for image noise reduction by using some fuzzy logic approaches. Unfortunately, most existing methods are still suffering from the high load computations which delay the processing time of images and the response of WMSNs applications as a real-time application.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Image Denoising Method for Wireless Multimedia Sensor Networks Based on DT-CWT

Sammouda

Al-Salman

Gumaei

et al. 2015

International Journal of Distributed Sensor Networks

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Wireless multimedia sensor network (WMSN) is a developed technology of wireless sensor networks and includes a set of nodes equipped with cameras and other sensors to detect ambient environment and produce multimedia data content. In this context, many types of noises occur due to sensors problems, change of illumination, fog, rain, and other weather conditions. These noises usually degrade the digital images acquired by camera sensors. Image denoising in spatial domain is more difficult and timeconsuming for real-time processing of WMSNs applications. In this study, an efficient method based on Dual-Tree Complex Wavelet Transform (DT-CWT) is developed to enhance the image denosing in WMSNs. This method is designed to reduce the image noises by selecting an optimal threshold value estimated from the approximation of wavelet coefficients. In our experiment, the proposed method was tested and compared with standard Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) on a set of natural scene images. Better results were achieved by using the DT-CWT in terms of image quality metrics and processing time.

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A Fuzzy Noise Reduction Method for Color Images

Cited by 74 publications

References 43 publications

Performance Analysis of Image de-noising using Fuzzy and Wiener Filter in Wavelet Domain

Performance Analysis of Image de-noising using Fuzzy and Wiener Filter in Wavelet Domain

Fuzzy Shrink Image Denoising Using Smoothing Spline Estimation

An Efficient Image Denoising Method for Wireless Multimedia Sensor Networks Based on DT-CWT

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