Study on Image Deblurring Mechanism Based on Autoregressive Moving Average Model
Guo Yagan · Video Engineering · 2015
In order to overcome the unstable with ambiguity blurring problem of these algorithm,as well as guarantee the clear and complete detail information of restoration image,and improve the computation speed of current image deblurring algorithm to achieve the goal of real time,the real-time stable mechanism for image deblurring based on the autoregressive moving average model is proposed. The active function is constructed by introducing the neural network and basing on the synaptic weights coefficient;then the fitness function is designed by the mean square error function of neural network; and embedding the artificial bee colony algorithm( ABC-Artificial Bees Colony) to train the neural network for finding the optimized weight value of neural network as well as the bias of active function to achieve global minimum; finally,the autoregressive moving average optimized model is designed to simultaneously identify fuzzy functions and fuzzy image to deconvolution the nonlinear deblurring image for eliminating the fuzzy.The performance of this algorithm is tested by simulation experiments. The results show that compared with other deblurring algorithms,the running speed of this mechanism is faster,and time consuming is the shortest; as well as the deblurring effect is the best,the detail information of restoration image is clearly visible.