CVGIP: Graphical Models and Image Processing
Input compression and efficient VLSI architectures for rank order and stack filters
Proceedings of of the IEEE winter workshop on Nonlinear digital signal processing
A Genetic Algorithm Approach to the Configuration of Stack Filters
Proceedings of the 3rd International Conference on Genetic Algorithms
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Design of optimal stack filters under the MAE criterion
IEEE Transactions on Signal Processing
An interactive co-evolutionary CAD system for garment pattern design
Computer-Aided Design
Expert Systems with Applications: An International Journal
An Improved Clonal Selection Algorithm and Its Application to Traveling Salesman Problems
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
An Expanded Lateral Interactive Clonal Selection Algorithm and Its Application
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Expert Systems with Applications: An International Journal
A review of clonal selection algorithm and its applications
Artificial Intelligence Review
Automatic multiple circle detection based on artificial immune systems
Expert Systems with Applications: An International Journal
A new fast algorithm for training large window stack filters
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
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Stack filters are a class of non-linear filters for suppressing the noise that is uncorrelated with the signal. Their design is formulated as a highly nonlinear optimization problem. A modified immune clonal selection algorithm, called immune memory clonal selection algorithm, is employed to perform the configuration of filters design. The new algorithm has the advantage of preventing from prematurity and fast convergence speed. As an experiment, the stack filters are used to restore images corrupted by uncorrelated additive noise with the level from 10% to 50%. The filters are trained on the small regions of the noise-free and noisy image and then applied to the whole image. The new algorithm has faster convergence speed than that of genetic algorithm. The results are compared with that using the median filter. It turns out that, with our proposed algorithm, a smaller MAE for all noise levels is achieved and much detailed information of the images is preserved. The results show that the new algorithm is effective and feasible.