An intelligent method to block e-mail bombs
Applied Intelligence
Restoring images with a multiscale neural network based technique
Proceedings of the 2008 ACM symposium on Applied computing
Efficient impulse noise reduction via local directional gradients and fuzzy logic
Fuzzy Sets and Systems
Modified Histogram Based Fuzzy Filter
MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
Fuzzy Sets and Systems
An intelligent typhoon damage prediction system from aerial photographs
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Fuzzy Classification of Restored MRI Images
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
A Fuzzy-Neural approach for estimation of depth map using focus
Applied Soft Computing
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Fuzzy based Impulse Noise Reduction Method
Multimedia Tools and Applications
Genetic fuzzy markup language for game of NoGo
Knowledge-Based Systems
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
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In this paper, we propose a Genetic-based Fuzzy Image Filter (GFIF) to remove additive identical independent distribution (i.i.d.) impulse noise from highly corrupted images. The proposed filter consists of a fuzzy number construction process, a fuzzy filtering process, a genetic learning process, and an image knowledge base. First, the fuzzy number construction process receives sample images or the noise-free image and then constructs an image knowledge base for the fuzzy filtering process. Second, the fuzzy filtering process contains a parallel fuzzy inference mechanism, a fuzzy mean process, and a fuzzy decision process to perform the task of noise removal. Finally, based on the genetic algorithm, the genetic learning process adjusts the parameters of the image knowledge base. By the experimental results, GFIF achieves a better performance than the state-of-the-art filters based on the criteria of Peak-Signal-to-Noise-Ratio (PSNR), Mean-Square-Error (MSE), and Mean-Absolute-Error (MAE). On the subjective evaluation of those filtered images, GFIF also results in a higher quality of global restoration.