Unsupervised adaptive neural-fuzzy inference system for solving differential equations
Applied Soft Computing
Finite element techniques for removing the mixture of Gaussian and impulsive noise
IEEE Transactions on Signal Processing
Fuzzy peer groups for reducing mixed Gaussian-impulse noise from color images
IEEE Transactions on Image Processing
Some improvements for image filtering using peer group techniques
Image and Vision Computing
A novel evolutionary approach to image enhancement filter design: method and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neurovision with resilient neural networks
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Fuzzy multipass filter for impulse noise removal in digital images
SITE'12 Proceedings of the 11th international conference on Telecommunications and Informatics, Proceedings of the 11th international conference on Signal Processing
Prediction of liquefaction potential based on CPT up-sampling
Computers & Geosciences
A hierarchical procedure for the synthesis of ANFIS networks
Advances in Fuzzy Systems
Direction based adaptive weighted switching median filter for removing high density impulse noise
Computers and Electrical Engineering
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In this paper, a novel adaptive network-based fuzzy inference system (ANFIS)-based filter, ABF, is presented for the restoration of images corrupted by impulsive noise (IN). The ABF is performed in two steps. In the first step, impulse detection is realized by using statistical tools. In the second step, a nonlinear filtering scheme based on ANFIS is performed for only the corrupted pixels detected in the first step. To demonstrate the effectivity of ABF at the removal of high-level IN, extensive simulations were realized for ABF and nine different comparison filters. Empirical results indicate that the proposed filter achieves a better performance than the comparison filters in terms of noise suppression and detail preservation, even when the images are highly corrupted by IN