A Image Restoration Based On Hopfield Neural Network

Image restoration is an important branch of digital image processing, and also is a difficult Point of image processing. Which primarily aim to improve quality of the given image, increase clarity of image component and eliminate fuzzy caused by degeneration system and noise pollution. Existing image restoration algorithms have some handling capacity for degeneration image, but have no enough for clarity, time complexity and storage complexity respective.The traditional methods to resolving image restoration all need calculate high dims, or generalized stationary process are satisfied in recovering process which make image restoration question encounter kinds of difficulty. Image restoration method of Hopfield-based neural network model transform image restoration into optimizes questions. Because neural network image restoration base on network energy function contraction, thus completely avoid question that inverse matrix bring about, and possess Wide applicability, however, dramatically increase time and storage complexity for creating neural network.In elementary neural network algorithm, the image gray level of the pixel can be represented by a simple sum of the neuron state variables. Even though the expression has outstanding fault-tolerant capability, it is not cost effective. If neuron state–variable group weighted scheme can express gray level of every pixel,then it can decrease the quantities of every pixel corresponding to neuron, so as to reduce the overall scale of neural network and guarantee good fault-tolerant capability. Because the algorithm adapt segmental linear function and the state-variables are hopingly valued, it can not guarantee network energy converge to a global minimum precisely which directly affect the quality of restored image, therefore, this paper adapt self feed continuous Hopfield neural network model and substitute continuous function for segmental linear function, thus make network energy minimal. Finally, this paper adapt overlapped block technique for restoring image, and reduce time and storage complexity.