Application of Multi-Scale Convolution Neural Network Optimization Image Defogging Algorithm in Image Processing

Abstract

To improve the ability to detect and identify smog images in complex road traffic scenes, smog images need to be defogged, and an optimized image defogging algorithm on the basis of multi-scale convolutional neural network (MCNN) is proposed. The physical model of road traffic scene smog scattering is constructed, and the image is divided sky area, road surface area and road sky boundary area. The road sky boundary line is the boundary line between road surface and sky area. The dark channel of traffic scene smog image is established by Canny edge detection and MCNN optimization, and the smog image is subjected to detail compensation and gray enhancement processing through prior knowledge. After substituting the atmospheric light value and transmittance map into the atmospheric scattering model (ACM), the MCNN learning model is combined to realize the filtering processing and defogging optimization of smog images in complex road traffic scenes. The color saturation, defogging degree, peak signal-to-noise ratio (PSNR), texture effect as well as other aspects of the image are taken as test indexes for the simulation experiment. The simulation results show that the color saturation, defogging degree and image definition of the defogged haze images in complex road traffic scenes are higher by using this method, which improves the output PSNR of the defogged haze images in complex road traffic scenes, and has a good application value in image defogging.