The Research Of Image Fusion Algorithm Based On Steerable Filters And Super-Pixel Segmentation

Image fusion process source images which contain the same scene information and extract useful information form these source images, the final fused image is constituted with these useful information so that it has better human visual effect and machine visual effect. At present, image fusion method is divided into two categories: the fusion method based on the spatial domain and the fusion method based on the transform domain. Images are fused directly on the gray scale image pixel space, if the fusion method based on the spatial domain is used; the fusion method based on the transform domain: Firstly, each source image is transformed separately. Secondly, transformed coefficients are fused according to a specific rule. Finally, the final fusion image is obtained by inverse transforming of transformed coefficients.The key of image fusion is that how to detect the useful information of source images effectively and accurately. After several methods about image information extraction were researched, the image fusion based on steerable filters and super-pixel segmentation was proposed and it has been studied. The edge and high-frequency details information of source images were detected and extracted by steerable filters, because the steerable filters had a strong sensitivity for edge information of image,and structure and low-frequency componentsinformation were detected and extracted by super-pixel segmentation. Finally,these information were fused effectively by the proposed fusion rules so that the final fusion image contained useful information which from different source images.The main work in this dissertation is listed as follows:(1) Knowledge about image fusion has been a general introduction;(2) The design idea of the steerable filter was described, and its design principle was introduced. At the same time, the effectiveness of the steerable filter for image edge detail information extraction was verified by experiments.(3) The super-pixel segmentation method based on entropy rate was researched, summarized the image segmentation principle with the random walks model on graphs of entropy rate, and introduced to the specific process of super-pixel segmentation problem were solved by greedy algorithm.(4) Steerable filter and super pixel segmentation were applied to the process of image fusion, and the direction response difference and two fusion rules which based on selecting point and selecting surface were proposed, so that the proposed algorithm is more reasonable, and improving the stability of the proposed algorithm.(5) Graphical User Interface was designed which made more convenient in experiments, such as selection of fusion methods and analysis of parameters.At the same time, the proposed algorithm was reasonable and effective that was verified by several experiments which contained two aspects: subjectiveevaluation based on visual and objective evaluation based on evaluation indexes,and the fusion result of proposed algorithm and the fusion results of other classical algorithms were compared and analyzed. Stability of the proposed algorithm was verified by source images which were fused with Poisson noise and Gaussian noise, salt and pepper noise and multiplicative noise. Finally,variable parameters of the proposed algorithm were analyzed by experiments,and the law, the influence of parameters on the result of the proposed algorithm,was summarized.