Detail-preserving median based filters in image processing
Pattern Recognition Letters
Weighted fuzzy mean filters for image processing
Fuzzy Sets and Systems
Communications of the ACM
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
IEEE Transactions on Fuzzy Systems
A new efficient approach for the removal of impulse noise from highly corrupted images
IEEE Transactions on Image Processing
Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization
IEEE Transactions on Image Processing
A universal noise removal algorithm with an impulse detector
IEEE Transactions on Image Processing
A hybrid neuro-fuzzy filter for edge preserving restoration of images corrupted by impulse noise
IEEE Transactions on Image Processing
A fuzzy operator for the enhancement of blurred and noisy images
IEEE Transactions on Image Processing
Quantum and impulse noise filtering from breast mammogram images
Computer Methods and Programs in Biomedicine
Intelligent noise detection and filtering using neuro-fuzzy system
Multimedia Tools and Applications
International Journal of High Performance Systems Architecture
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A new efficient approach to detect the impulse noise from the corrupted image using feed forward neural network (FFNN) is presented. A modified version of the arithmetic mean filter is proposed to remove the detected impulse noise. The performance of proposed noise detection approach is analyzed using the performance measures such as False Alarm Ratio (FAR), Missed Noise (MN) pixels and Falsely Detected Noise (FDN) pixels. The simulation results show that these performances are robust even at higher percentage of noise. The filtered result is compared with the other recent approaches in terms of Peak Signal to Noise Ratio (PSNR). The proposed method produces remarkably good results both in quantitative measures and qualitative judgments of image quality.