Research Of Wavelet Domain Image Fusion Algorithm And Its Application In Video Based Vehicle Detection System

With the development of world economics, intelligent transportation system has become more and more significant. As an important part of intelligent transportation system, vehicle detection system can detect and analyze traffic information automaticly through video processing.This dissertation researches the wavelet domain image denoising, registration and fusion algorithm; it designs a vehicle detection system which contains DSP based video processing cards and industrial personal computers, video processing cards are used to extract traffic information from video and image, industrial personal computers help users view and manage traffic information. Focusing on the conflict between computational complexity of wavelet domain image fusion algorithm and real-time capability of vehicle detection system, this dissertation has exploringly applied the wavelet domain image fusion algorithm to vehicle detection system, which improves the detection accuracy and meets the time-constraints of system requirements. The innovations involved in the research are as follow:(1) A wavelet modulus maximum threshold denoising method and an improved image registration algorithm based on corner feature are proposed. Based on the characteristics of random noise on different wavelet transform scales and the relationship between the noise Lipschitz and its wavelet modulus maximum, an image denoising algorithm is presented where the wavelet coefficients of noise signal are filtered by changeable threshold so as to reduce the noise; The improved image registration algorithm extracts the angular point by the gradient modulus, then it eliminates the mismatching points and gets all six raw parameters of the affine transformation equation. Some computer denoising and registration simulation results are given. The results show that the denoising method is effective both in removing the noise and in reserving the detail of image. It also shows that the improved image registration algorithm has high registration precision and low computational complexity.(2) This dissertation presents an image fusion algorithm based on local mean gradient modulus. After wavelet decomposition, the fused wavelet coefficients are conbined through the proportion of local mean gradient modulus of coefficients in each low frequency subimage. The improved local deviation fusion rule is used to fuse high frequency subimages, it defines the match measure by the local deviation and finally decides the fusion method for high frequence subimage. Experimental results show that the presented fusion method achieves comparatively high visual effects and performances than the traditional image fusion algorithm.(3) A design proposal of partition image fusion is brought forward in this dissertation. Focusing on the computational complexity of wavelet domain image fusion algorithm and its low real-time capability, this dissertation proposes a new method that fuses the given partition of images which is covered by the virtual detection line and ignores the the rest partition of this image. Comparing with fusion of the whole image, the partition image fusion has low computational complexity. Experimental results show that partition image fusion method applied in the vehicle detection system not only improves the image quality, but also meets the time-constraints of system requirements.