Abstract
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.