Image Processing Method Simulating Human Visual Mechanism

Image segmentation and image fusion are two important research fields of image processing. Image segmentation and image fusion are the basic work of target recognition, target tracking, target recognition. They are important for research. They have been the research focus for people.Due to the different application and different purpose, many segmentation methods are proposed. But there is not a method can solve all the segmentation problems. After all, the computer can not work as human. The algorithm design is also constrained. In order to make the computer segment the image like human, some computer vision theories are gradually introduced into the image segmentation. Many methods based on human visual mechanism are proposed, such as pulse coupled neural network model (PCNN). How to make these new algorithms smarter is the focus of the researchers.Image fusion is an information processing process, which refers to synthesize the images or sequence of images of a scene obtained by two or more sensors at the same time or different times, generating a new interpretation of the scene. It’s a destination of the image fusion that How to make the fusion algorithms closer to the work mode of the human vision, and make the fused image more in line with the standards of human perception. With the development of the human visual mechanism, some research results are introduced into the image fusion to promote the development of image fusion.In this thesis, image segmentation and image fusion are chose as the research object. To explore the new method of image segmentation and image fusion, to make it more in line with the standards of human vision, the human visual mechanism model is introduced as the research method. The main research works are as follows:(1) A new image segmentation method based on the pulse coupled neural network model is proposed. The method can simulate the human visual mechanism. In the method, the image pixel is regarded as the brain’s neurons. With the external input stimulus, the pixel generate the pulse during the the loop iteration, and then simulate the work mode of the human brain visual cortex. In the image segmentation process, the maximum variance ratio rule is introduced in the segmentation process to find the best segmentation point. The experimental analysis on the images whose histograms are unimodal, bimodal and multimodal respectively proves the validity of the proposed algorithm. And it also proves the simulation effect of human vision.(2) For the multi-images fusion, a new model named dual-layer PCNN model based on the standard PCNN model is proposed. This model can better simulate the work mechanism of human vision, and it is more in line with the human visual mechanism.(3) Three different image fusion methods based on wavelet transform, curvelet transform and contourlet transform is proposed, combined with PCNN model and dual-layer PCNN model. In the method based on the wavelet transform, when the image is decomposed by the wavelet transform, PCNN model and the energy of image local area gradient are used to select the coefficients. When the image is decomposed by curvelet transform or contourlet transform, the dual-layer PCNN model and local energy matching rule are used to select the coefficient. Experiments show that, three image fusion methods proposed in this thesis have good fusion effect, the fusion results consistent with human visual perception. Compared with the traditional image fusion methods, the proposed methods do more in line with the work mechanism of human vision in the image fusion process.