Growing Compact RBF Networks Using a Genetic Algorithm
SBRN '02 Proceedings of the VII Brazilian Symposium on Neural Networks (SBRN'02)
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem
Applied Soft Computing
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
A novel blind deconvolution scheme for image restoration usingrecursive filtering
IEEE Transactions on Signal Processing
Blur identification by the method of generalized cross-validation
IEEE Transactions on Image Processing
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Image deblurring is indispensable to many image processing applications. In this paper, we try to improve radiological images degraded during acquisition and processing. An autoregressive moving average (ARMA) model, used for nonlinearly degraded image deconvolution, is identified using a neural network (NN). The NN training is improved using a novel swarm optimization algorithm called Artificial Bees Colony (ABC), inspired from the foraging intelligence of honey bees. The ABC has the advantage of employing fewer control parameters compared to other swarm optimization algorithms. Both estimated image and blur function are identified through this representation. The optimized ARMA-NN model is then implemented on a Xilinx reconfigurable field-programmable gate array (FPGA) using hardware description language: VHDL. This VHDL code is tested on the rapid prototyping platform named ML505 based on a Virtex5-LXT FPGA chip of Xilinx. Simulation results using some test and real images are presented to sustain the applicability of this approach compared to the standard blind image deconvolution (BID) method that maximizes the likelihood using an iterative process. A statistical comparison is concluded based on performance evaluation using seven recent image quality metrics.