Research On The Key Technologies Of Image Processing Based On Multi-spectral Imaging System

Multi-spectral imaging system is mainly used for contrast imaging for several spectral bands of the same object, synchronously, in order to monitor and analysis the composition and distribution. Compared with the traditional single band imager, multi-spectral imager can obtain more spectral information; therefore, Multi-spectral imaging system has been widely used in many fields, such as geology exploring, object investigating, life state observing, and environmental monitoring. As a multi-spectral imaging system with a good performance, imaging clearly is at the forefront of the following information extracting and characteristic analyzing. However, noise interfering, optical system defocusing, platform vibrating and other related factors lead to the poor imaging quality, so it’s very necessary to solve above problem accordingly. Relative to the method of hardware adjustment, image processing play a more and more important role in the imaging system, especially in the multi-spectral imaging, because of the low cost, flexible method, real-time, improvement of large space, and so on.Several key technologies about auto-focusing, digital image stabilization and object recognition are researched systematically and some new algorithm of image processing are proposed accordingly in this paper, based on the multi-spectral platform which is researched and developed independently by our team. In addition, the software for the system is designed and realized, according to the research content and the device hardware characteristics.The main research contents and achievements in this paper are summarized as follows:The first, in the aspect of auto-focusing, several classic algorithms have been analyzed comparatively. Aimed at the existed defects of above algorithms, some new methods were proposed such as depth from defocusing based on logarithmic power spectrum, depth from focusing based on SUSAN edge detector, and depth from focusing based on differential projection. Through analysis and comparison, the third one is selected finally. In this method, firstly, calculate the projective values in x-direction and y-direction of the involved image’s focusing window data, derive the 1th norm of the two arrays’ first order differential values, compute the mean of the two set of 1st norm data, and make the Rms of the two means as the definition evaluation value of this image. And then, combined with the classic Mountain Climb-searching method, the auto-focusing process is finished. Finally, the performance of the method is tested and evaluated with the use of the system platform. Experimental results indicate that the auto-focusing method based on differential projection can be realized accurately, and has the same effect with the classic Brenner, Energy gradient and Roberts gradient algorithm, approximately. However, the running time of differential projection is only 0.67 times of Brenner, 0.33 times of Energy gradient, and 0.33 times of Roberts gradient. The method can meet the high-precision auto-focusing requirements very well.The second, in the aspect of digital image stabilization, a new algorithm based on the characteristic peak of projection matching is proposed. Firstly, several sub image blocks are divided about the reference frame and the current frame, and the horizontal projection and vertical projection of each sub image block are calculated according to the projection calculation formula. Secondly, the horizontal and vertical position differences of the corresponding sub blocks’ maximal projection characteristic peaks are calculated as motion vectors. And then, the motion vectors are corrected according to the motion vector variance ratio between frame and frame. Finally, the global motion vectors are derived based on the sub motion vectors. The characteristic peak of projection matching algorithm being applied to the multi-spectral imager, almost all of the search time is saved, in the premise to the same purpose. The ideal image sequence is obtained. In addition, the better real-time characteristic is obtained, too.The third, for the sake of the object recognition requirement, an improved C-V model is proposed, compared to the classical C-V model for image segmentation. In this model, the Dirac function’ parameter is corrected adaptively, by introducing the maximum value of distance function in each iteration. In this way, the effective range of active contour is broadened, and the number of iterations is reduced. The experimental results show that the ideal segmentation effect is obtained by the improved C-V model, with the iteration termination condition. Compared with the classic C-V model, the influence on segmentation, because of the initial contour position differences, is cut down. In addition, the convergence speed is improved 7 times. The characteristics of real time and global nature both become better. Therefore, the robustness of multi-spectral imager segmentation is improved, accordingly.The fourth, according to the research content and the device hardware characteristics, the control software is developed and realized comprehensively. And then, the following functions is realized: the real-time acquisition and display of multi-spectral data, manual focusing and automatic focusing, manual exposure and automatic exposure, digital image stabilization, image segmentation and target recognition, and storage of images and videos. Finally, the system runs normally with the help of the software.