Impulse Noise Removal From Digital Images by a Detail-Preserving Filter Based on Type-2 Fuzzy Logic

Yildirim, M.T.; Baştürk, Alper; Yüksel, Mehmet Emin

doi:10.1109/tfuzz.2008.924358

Cited by 59 publications

(36 citation statements)

References 59 publications

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Section: Discussionmentioning

confidence: 99%

“…In [35], a filter based on T2 FLS techniques was presented, and in [36] a filter based on an adaptive T2 fuzzy median was proposed. Finally, a selective feedback fuzzy neural network based on IT2 FLS was introduced in [35]. In the research papers [8,[34][35][36][37], filtering accuracy is validated using visual appreciation, mean squared error (MSE), mean absolute error (MAE), the peak signal to noise ratio (PSNR), and normalized mean square error (NMSE).…”

Section: Discussionmentioning

confidence: 99%

“…In [8,35,36], images were corrupted by impulse noise ranging from 5% to 75%. Only in [8], tests are performed on color images using validation metrics MSE and MPE.…”

Section: Discussionmentioning

confidence: 99%

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Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and represent uncertainty that is more complex. This paper is focused on image processing systems, which includes image segmentation, image filtering, image classification and edge detection. Various applications are presented where general type-2 fuzzy sets, interval type-2 fuzzy sets, and interval-value fuzzy sets are used; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing. In all accounts, it is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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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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“…However, creating the rule-base structure is quite difficult for highly corrupted images. For this reason, many methods [25][26][27][28][29] related to the adaptive neuro-fuzzy inference system (ANFIS) are presented to prevent this difficulty. When ANFIS-based methods are trained properly, they can maintain the details of the images during detection and suppression of the noise.…”

Section: Introductionmentioning

confidence: 99%

Removing random-valued impulse noise in images using a neural network detector

Turkmen¹

2014

Turk J Elec Eng & Comp Sci

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This paper proposes a new method using an artificial neural network to remove random-valued impulse noise (RVIN) in images. The inputs of the neural model used to detect the RVIN are formed using basic and related gradient values. The detection of the noisy pixels is realized in 3 phases using the proposed neural detector. In order to obtain a more robust detector, 2 different networks, which are trained with an artificial training image corrupted with high and low clutter densities, are used. The extensive simulation results show that the proposed method is significantly better than the compared filters in terms of its image restoration and noise detection performance.

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Impulse Noise Removal From Digital Images by a Detail-Preserving Filter Based on Type-2 Fuzzy Logic

Cited by 59 publications

References 59 publications

Review of Recent Type-2 Fuzzy Image Processing Applications

Review of Recent Type-2 Fuzzy Image Processing Applications

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

Removing random-valued impulse noise in images using a neural network detector

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