Grayscale image enhancement method based on stochastic resonance mechanism of delayed self-feedback FHN (fitzhugh-nagumo) model

Abstract

The invention relates to a grayscale image enhancement method based on a stochastic resonance mechanism of a delayed self-feedback FHN (fitzhugh-nagumo) model. A traditional method has no good effect in absence of noise model prior knowledge. The grayscale image enhancement method includes steps of performing row scan for the noise-contained grayscale images at first, acquiring one dimensional sequence formed by various pixel gray values, and normalizing various factors of the one dimensional sequence; inputting the normalized one-dimensional sequence into the delayed self-feedback FHN neuron model so as to enable system output to achieve the best state of the stochastic resonance; restoring the outputted sequence as an image pixel value range in an inverted normalization manner, and restoring the image pixel value range as grayscale images; and then performing column scan for the restored grayscale images by repeating the process, and finally acquiring the reinforced grayscale images after row scan and column scan. Since dimension reduction of the images is realized in a row direction and a column direction, peer to peer relationship of two-dimensional images in row and column directions is effectively improved, and retention of space structure characteristics of the grayscale images which are processed by the stochastic resonance is facilitated.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a grayscale image enhancement method based on a stochastic resonance mechanism of a delayed self-feedback FHN neuron model. Background technique [0002] Image is the main medium for people to transmit information, but in the process of image acquisition, transmission, encoding and acquisition, it will be interfered by various noises, resulting in the degradation of image quality, which is very unfavorable for higher-level analysis or understanding of images . Therefore, it is particularly important to effectively eliminate the harmful effects of noise and at the same time enhance the useful signal in the image. At present, the traditional image enhancement methods mainly include: image enhancement methods based on histogram equalization, image enhancement methods based on wavelet transform, mean filtering and other algorithms. These methods enhance the image information by removi...

AI Technical Summary

Problems solved by technology

These methods enhance the image information by removing the noise signal in the image, especially when the signal-to-noise ratio of the image signal is low, while removing the noise, the useful information of the image will inevitably be lost, so the image enhancement effect is not ideal.

Images