Image histogram equalization enhancement based on PCNN

In order to enhance images more effectively, a novel enhancement strategy is presented, which is processed by two stages: local enhancement stage and global enhancement stage. In local enhancement stage PCNN (pulse coupled neural network) is used to simulate spatial concealment effect and abnegate the double-edge which is difficult for human eyes to observe. Meantime lateral inhibition is introduced to simulate Mach band effect, which can enlarge the difference of bilateral gray values of edges and can smooth the flat zones. In global enhancement stage, both gray value information and spatial information are coupled into the inner activity item, and the threshold of the corresponding neuron is set as the cumulative density function of the histogram of local enhanced image. Thus through comparing the inner activity item and the cumulative density function, final enhanced image can be attained. Both theory and experiments demonstrate that this method can equalize the given image perfectly, and not only it is immune from traditional gray scale loss problem, but also its histogram is better equalized than traditional methods.