Eckhorn obtained a model of a mammalian neuron, the pulse-coupled neural network model (PCNN), by studying neurons in the cat's visual cortex by examining their synchronized pulse oscillations. PCNN is a single-layer neural network model. The dynamic threshold of the model is adjustable, and it has the characteristics of nonlinear modulation coupling, synchronous pulse and dynamic pulse excitation, which makes PCNN model have a good effect on image feature extraction, edge information analysis, image enhancement and image segmentation. In this paper, an image enhancement model called AFC-MSPCNN is proposed for image processing. Aiming at the problems of complex structure of pulse coupled neurons, poor adaptive performance and poor processing results caused by repeated attenuation of image signal threshold in experiments. The improved model reduces the computational complexity and enhances the adaptive capability to some extent. We apply the new model AFC-MSPCNN to image enhancement and experimentally verify its good enhancement effect.
Mural restoration has an extremely important significance in image restoration. Image enhancement aims to improve image quality, clarity or visualization. The pulse-coupled neural network (PCNN) is a single-layer neural network structure for artificial neural networks, with featuring nonlinear coupling modulation, synchronized pulses, and dynamic pulse excitation. The unique structure and working principle of PCNN enable it to perform well with strong spatial and temporal correlations in image enhancement aspect. The synaptic-linked-FCMSPCNN(SLFC-MSPCNN) is proposed in this paper, and achieves more effective control of neuron firing time by adjusting adaptive parameters. Through related experiments, The proposed SLFC-MSPCNN has good image enhancement performances comparing with popular previous image enhancement approaches in Dunhuang Murals.
Most popular image enhancement algorithms are generally based on a series of specialized images collected by image photography devices. Hereinto, Pulse-Coupled Neural Network (PCNN), plays important roles in image enhancement aspect, with lower computational complexity and higher image enhancement accuracy. On the research, we propose an image enhancement method based on enhanced fire-controlled MSPCNN(EFC-MSPCNN) model, which gives the setting methods of designed adaptive parameters. Related experimental results demonstrate that our proposed method has good image enhancement performances.
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