A fuzzy filter for the removal of random impulse noise in image sequences

Mélange, Tom; Nachtegael, Mike; Schulte, Stefan; Kerre, Etienne

doi:10.1016/j.imavis.2011.01.005

Cited by 28 publications

(29 citation statements)

References 33 publications

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“…All the above algorithms presented here are implemented using MATLAB software tool and the performance metric results have been compared and depicted below (Melange et al, 2011;Hsieh et al, 2013). The performance metrics like PSNR, MAE, NCC and IEF are obtained using four structuring elements with 50% impulse noise intensity as shown in Table 1.…”

Section: Resultsmentioning

confidence: 99%

A Case Study of Impulse Noise Reduction Using Morphological Image Processing with Structuring Elements

Elamaran¹,

Upadhyay²,

Narasimhan³

et al. 2015

Asian J. of Scientific Research

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Image enhancement plays a vital role in the field of digital image processing since the noise is added very often with the original image. Spatial filtering techniques like low pass, high pass, band pass and notch with the help of convolution mask are often used to enhance the image with reduced noise. Recently, morphological algorithms play a major role in the area of filtering noise, boundary detection, shape detection, image manipulation, etc. Especially by applying dilation, erosion, opening and closing to the image appropriately, the quality of the image can be further enhanced. In this study, morphological algorithms are being applied to remove the salt and pepper noise from the input image. Erosion followed by dilation and dilation followed by erosion are the main methods to remove this kind of impulsive noise. MATLAB software is used to apply these opening and closing methods to enhance the quality of the image with difference structuring elements. Simulation results are obtained and the comparison is done with the performance metrics like Peak Signal-to-Noise Ratio (PSNR), Mean Absolute Error (MAE), Normalized Correlation Coefficient (NCC) and Image Enhancement Factor (IEF) with different structuring elements.Key words: Image enhancement, closing, impulse noise, morphological filtering, opening INTRODUCTIONIn the field of image processing, modern trend moves towards handling images effectively with more clarity and high performance. This image enhancement can be done either in spatial domain or in frequency domain. Low pass, high pass, band pass and notch filters are designed to remove the noise from the image or to enhance the quality of the image (Chen et al., 2013). If the filter or kernel size is small, filtering can be done more effectively in the spatial domain (Islam et al., 2010). If the kernel or convolution mask size becomes large, this would be tedious in the spatial domain. In such a case, the filtering could be done more effectively in the frequency domain. Since the convolution becomes multiplication here, the processing would be done more effectively even with large kernel size (Elamaran et al., 2013;Malik and Baharudin, 2012).In some applications, there is a necessity to detect the shape and structure of the image or a pattern within an image. Morphological algorithms are more pertinent for this kind of image analysis (Li et al., 2013). A particular shape can be extracted and analyzed with the help of dilation and erosion processes. A particular pattern size can be enlarged by dilation and minimized by erosion. By combining both dilation and erosion, morphological filtering would be achieved. Input image can be smoothened along with the removal of noise by opening and closing. Edge detection, feature detection, image segmentation, counting objects is the other applications of morphological algorithms. These operations are not linear like spatial filtering using convolution masks (Elamaran et al., 2012;Dougherty, 2009).

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Section: Resultsmentioning

confidence: 99%

A Case Study of Impulse Noise Reduction Using Morphological Image Processing with Structuring Elements

Elamaran¹,

Upadhyay²,

Narasimhan³

et al. 2015

Asian J. of Scientific Research

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“…More measures can be used, as can be seen in our previous works on denoising using fuzzy mathematical morphology [24,25,26,27]. In [28] the authors used the PSNR and the correlation, Srinivasan and Ebenezer in [5] measure the performance of the algorithm using the PSNR and the "image enhancement factor" (IEF), and in [29,30] together with the PSNR the performance of the proposed algorithms is measured using the "mean absolute error" (MAE). The structural similarity index measure (SSIM) is used also in [31].…”

Section: Simulation Resultsmentioning

confidence: 99%

High-density impulse noise removal using fuzzy mathematical morphology

González-Hidalgo¹,

Massanet²,

Mir³

et al. 2013

Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology

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This paper proposes a filtering method for highdensity impulse noise removal based on the fuzzy mathematical morphology using t-norms. The method is a two phased method. In the first phase, an impulse noise detector based on the fuzzy tophat transforms is used to identify pixels which are likely to be contaminated by noise. In the second phase, the image is restored using a specialized regularization method using fuzzy open-close or fuzzy close-open sequences applied only to those selected contaminated pixels and applying then a block smart erase algorithm. Experimental results show that the proposed algorithm presents a better performance in terms of edge preservation and noise suppression than other nonlinear filtering methods, including the presented in [1], in which this method is based on.

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“…If an image is being sent electronically from one place to another via satellite or wireless transmission or through networked cables, the expecting errors may be occurred in the image signal. These errors will appear on the image output in different ways depending on the type of disturbance in the signal [14]. The type of noise and errors in the image can be expected as:…”

Section: B B T Ty Yp Pe Es S O Of F N No Oi Is Se Es Smentioning

confidence: 99%