Based On The Real Image Reappearance Of Pulse Coupled Neural Network Research

The purpose of realistic image rendition is to provide ideal images corresponding to the human visual perception. Physiologic studies show that, despite the various ambient illuminant, the human visual system can also maintain the correct perception of scenes performs. Therefore, it is very appealing to apply the physiological researches for guiding image processing and promoting image processing and computer vision. Under the framework of the visual neural network, this paper proposes an algorithm of realistic image rendition combined PCNN with the retinal ganglion cell’s ON-OFF opponent mechanism.Pulse-Coupled Neural Networks is a visual cortex-inspired neural network constructed based on the mammal visual principle, and the synchronizing pulse grant characteristic and global coupling processing mechanism of PCNN make it be more in line with the physiological basis of the human visual system. PME is a dynamic description of the center-surround opponent receptive fields, and provides a simulation of the retinal ganglion cell’s ON-OFF opponent mechanism, PME has the attribute of performance which is suitable for color image enhancement. Therefore, combining PCNN with PME to guide realistic image rendition has high theoretical value and application prospect.The main content of this paper is summarized as follows:Firstly, several algorithms based on visual characteristics are discussed, the related visual characteristics are generalized, and the ON-OFF opponent mechanism of retinal receptive fields are specified, and the advantages and shortcomings of these models are analyzed. Moreover, Retinex, the improved algorithm and the algorithm based on neurodynamics model are highlighted with a deeper understanding, the characteristics of the visual system and color constancy of Retinex is conformed. The center-surround opponent principle of the retina to realistic image rendition field is introduced by the neural dynamics algorithm. The corresponding experiments and analysis verify the effectiveness of the algorithms.Secondly, the principle and basic characteristics of PCNN are described in detail, and the image enhancement method based on PCNN is studied in-depth. Experiments of gray-scale and color image enhancements show that this method is perfect, and conforms human visual characteristics.Thirdly, the simplified model of PCNN—Intersecting Cortical Model is researched, the basic principles and characteristics of ICM is provided. Since the parameters are difficult to set, the model is improved with keeping the basic characteristics unchangeable, and then the improved model is applied to image enhancement, the corresponding experiments and analysis verify the effectiveness of the algorithm..Finally, based on the neural dynamics algorithm and PCNN algorithm, a novel method of realistic image rendition combining PCNN with PME is proposed. PCNN is included in the framework of neurodynamics, the meaning of the combination of both, the overall framework of the model and the performance attributes are discussed in detail. Compared with other realistic image rendition methods which are based on visual characteristics, the experiments verify the validity of the new algorithm. Then the four key parameters of the model are discussed through experiments respectively, and the optimal parameter settings are obtained.