Parallel VLSI design for a real-time video-impulse noise-reduction processor
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A Variational Approach to Remove Outliers and Impulse Noise
Journal of Mathematical Imaging and Vision
Practical, Unified, Motion and Missing Data Treatment in Degraded Video
Journal of Mathematical Imaging and Vision
Adaptive fuzzy switching filter for images corrupted by impulse noise
Pattern Recognition Letters
An Algorithm for Adaptive Mean Filtering and Its Hardware Implementation
Journal of VLSI Signal Processing Systems
Eye-tracker data filtering using pulse coupled neural network
MS'06 Proceedings of the 17th IASTED international conference on Modelling and simulation
Suppression of Impulse Noise in Medical Images with the Use of Fuzzy Adaptive Median Filter
Journal of Medical Systems
Impulse noise removal utilizing second-order difference analysis
Signal Processing
Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter
Digital Signal Processing
Removing impulse bursts from images by training-based filtering
EURASIP Journal on Applied Signal Processing
Impulsive noise suppression from images by using Anfis interpolant and lillietest
EURASIP Journal on Applied Signal Processing
Impulsive noise suppression from images with the noise exclusive filter
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
An online noise filter for eye-tracker data recorded in a virtual environment
Proceedings of the 2008 symposium on Eye tracking research & applications
MR Images Restoration With the Use of Fuzzy Filter Having Adaptive Membership Parameters
Journal of Medical Systems
Image restoration based on Laplacian preprocessed long-range correlation
Multidimensional Systems and Signal Processing
Vector median filter for removal of impulse noise from color images
ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
A method based on rank-ordered filter to detect edges in cellular image
Pattern Recognition Letters
Minimum-Maximum Exclusive Interpolation Filter for Image Denoising
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Efficient impulse noise reduction via local directional gradients and fuzzy logic
Fuzzy Sets and Systems
Removal of Impulse Noise in Images by Means of the Use of Support Vector Machines
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Fuzzy peer groups for reducing mixed Gaussian-impulse noise from color images
IEEE Transactions on Image Processing
Geometric features-based filtering for suppression of impulse noise in color images
IEEE Transactions on Image Processing
A fast efficient restoration algorithm for high-noise image filtering with adaptive approach
Journal of Visual Communication and Image Representation
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
An adaptive fuzzy switching filter for images corrupted by impulse noise
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Ultrasound despeckling for active contour segmentation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Adaptive kernel-based image denoising employing semi-parametric regularization
IEEE Transactions on Image Processing
Ultrasound despeckling for contrast enhancement
IEEE Transactions on Image Processing
Switching bilateral filter with a texture/noise detector for universal noise removal
IEEE Transactions on Image Processing
EURASIP Journal on Advances in Signal Processing
Impulse noise filtering using robust pixel-wise S-estimate of variance
EURASIP Journal on Advances in Signal Processing - Special issue on robust processing of nonstationary signals
Document image analysis: issues, comparison of methods and remaining problems
Artificial Intelligence Review
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
Evolutionary tree-structured filter for impulse noise removal
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Impulse noise detection based on robust statistics and genetic programming
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Image filtering using support vector machine
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Information Sciences: an International Journal
WSEAS Transactions on Signal Processing
On the design of neighboring fuzzy median filter for removal of impulse noises
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
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A new framework for removing impulse noise from images is presented in which the nature of the filtering operation is conditioned on a state variable defined as the output of a classifier that operates on the differences between the input pixel and the remaining rank-ordered pixels in a sliding window. As part of this framework, several algorithms are examined, each of which is applicable to fixed and random-valued impulse noise models. First, a simple two-state approach is described in which the algorithm switches between the output of an identity filter and a rank-ordered mean (ROM) filter. The technique achieves an excellent tradeoff between noise suppression and detail preservation with little increase in computational complexity over the simple median filter. For a small additional cost in memory, this simple strategy is easily generalized into a multistate approach using weighted combinations of the identity and ROM filter in which the weighting coefficients can be optimized using image training data. Extensive simulations indicate that these methods perform significantly better in terms of noise suppression and detail preservation than a number of existing nonlinear techniques with as much as 40% impulse noise corruption. Moreover, the method can effectively restore images corrupted with Gaussian noise and mixed Gaussian and impulse noise. Finally, the method is shown to be extremely robust with respect to the training data and the percentage of impulse noise