Genetic-based fuzzy image filter and its application to image processing

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, a...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2005-08, Vol.35 (4), p.694-711
Main Authors: Lee, Chang-Shing, Guo, Shu-Mei, Hsu, Chin-Yuan
Format: Article
Language:English
Subjects:
Adaptive filters
Additive noise
Algorithms
Artificial Intelligence
Cluster Analysis
Computer Simulation
Construction
Filtering
Filtration
Fuzzy
Fuzzy inference
Fuzzy Logic
fuzzy number
Fuzzy set theory
genetic algorithm
Genetic algorithms
Image communication
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Image restoration
impulse noise
Inference algorithms
Information Storage and Retrieval - methods
Knowledge bases (artificial intelligence)
Models, Statistical
Noise
Numerical Analysis, Computer-Assisted
Pattern Recognition, Automated - methods
PSNR
Signal Processing, Computer-Assisted
Studies
Working environment noise
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Summary: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.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2005.845397