Research On The Method For Different-source Image Fusion And Its Evaluation

Different-source image fusion is the process of forming a high-performance sensing system by collaborating different types of image sensors and gathering all kinds of image information effectively in order to obtain a coherent description of the same object. This technology processes the information from multiple perspectives and combines complementary information and redundant information of several different images. The working available range of sensors can be expanded. The reliability and maintainability of the sensing system can be improved. The requirements of the single sensor can be reduced. The description of information of the target or the scene can be more accurate, reliable and comprehensive, which not only benefit the visual observation process by human, but also provide more effective information for further image processing.The infrared and visible images are selected as the study objects, which are representative images from different source images. Based on infrared technology, image processing and artificial intelligence, two kinds of image fusion algorithms for different source images, including spatial domain and transform domain, are designed with the help of analyzing and absorbing the successful experience from home and abroad. A novel method for image fusion quality evaluation is established. According to this newly-designed criterion, a closed-loop adaptive image fusion scheme is proposed. The scheme can select the most optimized fusion program automatically. The hierarchical structure ensures the speed and accuracy of the fusion algorithm. The degree of automation is thereby enhanced.An image fusion algorithm based on principle component analysis is presented. Current image fusion algorithms of spatial domain are analyzed and compared, such as weighted average method and gray-level extreme value method. After that, an automatic weighted average method based on K-L (Karhunen-Loeve) transform is proposed. This algorithm preserves the practicability of the current weighted average method. Through the principle component analysis, useful information is concentrated into the independent new matrixes. The weighted coefficients of original images in weighted average method are calculated correspondingly, so that a better fusion result is achieved. Experimental results show that on the use of this image fusion algorithm based on K-L transform, the fused image is superior to the images of the existing fusion method of spatial domain.Image fusion methods of transform domain is researched. The typical multi-scale analysis theory in transform domain is introduced, especially the representative wavelet transform technology and its application in image fusion. Experiments are carried through in order to deduce the advantages and disadvantages of wavelet transform. Based on the analysis above, the image fusion methods of anisotropic multi-scale analysis is emphasized. The NSCT (Non-sampled Contourlet Transform) is used as the main tool. The application of NSCT in image fusion is proposed and a novel fusion strategy is designed correspondingly. A new image fusion method based on NSCT and PCNN (Pulse Coupling Neural Network) is developed. This method takes advantage of the properties of anisotropic, multi-directional and shift invariance of NSCT. The frequency-aliasing problem of the previous multi-scale decomposition is eliminated. The general coupling characteristic of PCNN is also adopted. The view effect of the fused image can be improved with the help of its biological background. Experimental results show that the fused images based on the new method are clear and natural, which is suitable for the human observation. Compared with the current wavelet method, the better fusion effect is achieved.Comprehensive evaluation method for different source image fusion quality is studied. Based on the analysis of the existing subjective and objective image evaluation methods, multiple evaluation factors are classified. The factor selection rule and the limitation of single factor are also discussed. Thereafter, current integration methods for multiple factors are researched and compared. Based on the information above, a mathematical model for image fusion evaluation is designed on the foundation of FNN (fuzzy neural network), which combines the structural knowledge expression ability of fuzzy logic and the self-learning ability of neural network. Multiple typical image fusion objective evaluation factors are fuzzificated with the model. The subjective evaluation results are used as the prior knowledge. The weight factors of the evaluation parameters are generated by the network study. The efficiency of the network study is improved through a momentum factor. Experimental results show that the newly-developed evaluation method can conduct a comprehensive and accurate evaluation, which overcomes the one-sidedness from the human subjective judgments and the single objective evaluation factors.Adaptive image fusion scheme for different source images is investigated. Based on the above new comprehensive evaluation method, a close-loop adaptive image fusion model is established. The layers in the model are assorted serially according to the calculation complexity of fusion algorithms, including multiple fusion methods of various features. The scheme can select the optimized fusion scenario according to the user requirement. The shortcoming of the shortage of flexibility of the open-loop system is overcome. Furthermore, the algorithms in the model can be supplemented easily, which has a good expandability. In order to verify this model, weighted average fusion method, wavelet-based fusion method, Contourlet-based fusion method and NSCT-PCNN-based fusion method are selected as the algorithm group of the experiment. Experimental result shows that the new scheme set up the relationship between fusing result and the user requirement. The total fusion process is unmanned and the automation degree is improved.