Detail-Preserving Restoration of Impulse Noise Corrupted Images by a Switching Median Filter Guided by a Simple Neuro-Fu
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Detail-Preserving Restoration of Impulse Noise Corrupted Images by a Switching Median Filter Guided by a Simple Neuro-Fuzzy Network ¨ M. Emin Yuksel Digital Signal and Image Processing Laboratory, Department of Electronics Engineering, Erciyes University, Kayseri 38039, Turkey Email: [email protected]
¨ Alper Bas¸turk Digital Signal and Image Processing Laboratory, Department of Electronics Engineering, Erciyes University, Kayseri 38039, Turkey Email: [email protected]
Erkan Bes¸dok Department of Photogrammetry Engineering, Erciyes University, Kayseri 38039, Turkey Email: [email protected] Received 25 August 2003; Revised 28 December 2003 A new operator for the restoration of digital images corrupted by impulse noise is presented. The proposed operator is a simple recursive switching median filter guided by a neuro-fuzzy network functioning as an impulse detector. The internal parameters of the neuro-fuzzy impulse detector are adaptively optimized by training. The training is easily accomplished by using simple artificial images that can be generated in a computer. The most distinctive feature of the proposed operator over other operators is that it offers excellent detail- and texture-preservation performance, while effectively removing noise from the input image. Extensive experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image. Keywords and phrases: image processing, noise filtering, median filter, neuro-fuzzy systems.
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INTRODUCTION
Digital images are valuable and important sources of information in many research and application areas including astronomy, biology, medicine, remote sensing, materials science, and so on [1, 2]. During image acquisition and/or transmission, digital images are often contaminated by impulse noise due to a number of nonidealities in the imaging process. The noise is usually caused by either an imperfect medium between the original scene and the imaging system (random scattering and absorption) or a nonideal imaging system (sensor noise, limited system accuracy, finite precision and quantization of image data, etc.). The noise usually corrupts images by replacing some of the pixels of the original image with new pixels having luminance values near or equal to the minimum or maximum of the allowable dynamic luminance range. In most applications, it is very important to remove impulse noise from image data, since the performances of
subsequent image processing tasks are strictly dependent on the success of image noise removal operation. However, this is a difficult problem in any image processing system because the restoration filter must not distort the useful information in the image and preserve image details and texture while removing the noise. Conventional noise cancellation filters usually have the drawback of introducing undesirable distortions and blurring effects into the output image during noise cancellation process [1, 2]. A large number of me
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