M. Emin Yüksel

Detail-preserving restoration of impulse noise corrupted images by a switching median filter guided by a simple neuro-fuzzy network

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.

Application of autoregressive analysis to 20 MHz pulsed Doppler data in real time

The real time application of autoregressive (AR) spectral analysis to a 20-MHz pulsed Doppler blood flowmeter is presented. The system consists of a TMS 320C25 digital signal processor with a 80286 based PC/AT microcomputer and associated interfacing circuitry. The AR method was used for in vivo spectral analysis of the signals obtained from a 20-MHz pulsed Doppler flowmeter in real time. The data obtained from digital and coronary arteries were processed using both AR and FFT spectral analysis methods. Also the data obtained from a stenosis coronary artery under surgical operation were processed using both methods. When the results are compared, it is seen that autoregressive analysis has given better results. Therefore the technique can be used in the examining of small vessels such as renal, iliac, mesenteric, coronary and digital arteries.

Inspection of defects in fabrics using Gabor wavelets and principle component analysis

In this paper, a new method for inspection of textile defects in fabrics is presented. The method is based upon the extraction of fabric features by Gabor wavelets. The Gabor wavelets transform provides an effective way to analyze images and extract features of textures. Principal component analysis using singular value decomposition is used to reduce the dimension of feature vectors. Performance of the method has been tested with defective fabric images taken from TILDA textile texture database. Experiments show that these defects are detected accurately.

A type-2 fuzzy logic filter for detail-preserving restoration of digital images corrupted by impulse noise

A novel filtering operator based on type-2 fuzzy logic is proposed for detail preserving restoration of images corrupted by impulse noise. The performance of the proposed operator is evaluated for different test images corrupted at various noise densities and also compared with representative impulse noise removal operators from the literature. Results of the filtering experiments show that the presented operator offers superior performance over the competing operators by efficiently suppressing the noise in the image while at the same time effectively preserving the useful information in the image.

Adaptive neuro-fuzzy inference system for speckle noise reduction in SAR images

An adaptive neuro-fuzzy inference system (ANFIS) based method is proposed for speckle noise reduction in synthetic aperture radar (SAR) images. Before using active RADAR (radio detection and ranging) and SAR imageries, the very first step is to reduce the effect of speckle noise. Reduction of speckle noise is one of the most important processes to increase the quality of radar coherent images. Filtering is the common method which is used to reduce the speckle noise. For this purpose, two ANFISs are trained and outputs of these systems are converted to one output through a mean calculator in this work. Performance of the proposed method is compared with performances of state-of-the-art methods in the literature for speckle noise reduction. Results are presented by filtered images and a table.

Efficient distortion reduction of mixed noise filters by neuro-fuzzy processing

A simple method for reducing undesirable distortion effects of mixed noise filters for digital images is presented. The method is based on a simple 2-input 1-output neuro-fuzzy network. The internal parameters of the neuro-fuzzy network are adaptively optimized by training. The training is easily accomplished by using simple artificial images generated on a computer. The method can be used with any type of mixed noise filters since its operation is completely independent of the filter. The proposed method is applied to two representative mixed noise filters from the literature under different noise conditions and image properties. Results indicate that the proposed method may efficiently be used with any type of mixed noise filters to effectively reduce their distortion effects.

Output Enhancement of Impulse Noise Filters by Edge Detection and Neuro-fuzzy Processing

A simple method for enhancing the output images of impulse noise filters for digital images is presented. The method is based on an edge detector and a simple 3-input 1-output neuro-fuzzy network. The internal parameters of the neuro-fuzzy network are adaptively optimized by training. The training is easily accomplished by using simple artificial images generated in a computer. The method can be used with any type of impulse noise filters since its operation is completely independent of the filter. The proposed method is applied to four representative impulse noise filters from the literature under different noise conditions and image properties. Results indicate that the proposed method may efficiently be used with any type of impulse noise filters to effectively reduce its distortion effects and enhance its output.

A Type-2 Fuzzy Logic Operator for Impulse Noise Removal from Digital Images

In this paper, a new detail-preserving neuro-fuzzy (NF) filtering operator based on typc-2 fuzzy logic tccniqucs for restoring digital images corrupted by impulse noise is presented. The operator is constructed by combining desired number of typc-2 NF filters, defuzzifiers and a postprocessor. All NF filters in the structure of the operator are Sugeno type first order type-2 interval fuzzy inference systems. Internal structures of the NF filters are identical to each other. Simulation results indicate that the proposed operator offers superior performance in removing impulse noise from images while effectively preserving image details and texture.