Optimal Image Restoration Using Swarm Algorithms And Their Synergy
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The novel approach introduced here is taking advantage of swarm intelligence to facilitate optimization process in TV regularization. An ARMA model used for the nonlinearly degraded image deconvolution is identified using ANN which is fast trained by two swarm algorithms hybridization: PSO and BFO. Both estimated image and blur function are identified through this representation. Some applications on radiological images are presented in simulation results. This optimized model will be implemented on reconfigurable hardware. Computational comparison based on image quality reached is made betwee...
The novel approach introduced here is taking advantage of swarm intelligence to facilitate optimization process in TV regularization. An ARMA model used for the nonlinearly degraded image deconvolution is identified using ANN which is fast trained by two swarm algorithms hybridization: PSO and BFO. Both estimated image and blur function are identified through this representation. Some applications on radiological images are presented in simulation results. This optimized model will be implemented on reconfigurable hardware. Computational comparison based on image quality reached is made between different approaches.
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