Research On Key Technologies Of Image Fusion

Image Edge detection and image registration is a key step and the necessary prerequisite of image fusion. Image edge detection and image registration techniques are studied and some new algorithms are applied to image edge detection and image registration in this paper, to prepare for sequence of image processing and image fusion.In the image, edge characteristics is very important and easy acquired characteristics, there are many of edge detection algorithms, such as Sobel operator, Canny operator, Log operator are being applied to image edge detection. But these algorithms are very sensitive to noise, although improved Canny operator has greatly increased in anti-noise and relatively clear edge that can be extracted, but the detection speed of the algorithm is slower, so that can not be used in the image processing sequence. To find a testing speed, strong anti-noise, high detectable precision and better edge-details protection algorithm, set pair analysis and degree connection situation are applied to the image edge detection in this paper. firstly, the degree of identity ,opposition and discrepancy of the eight directions of the pixel are calculated with set pair analysis, secondly, the IDC (Identical-Discrepancy-Contrary) connection of the pixel is ordered in order to Identical-Balanceable-Contrary trend with degree connection situation, and then determine whether the point is the edge by trend of the pixel. In the others, contrast is not only poor, but also characteristic of image edge is fuzzy to some images, in order to increase the image contrast and highlight characteristic of image edge, gray-scale transformation of the image is operated before image edge detection. Our experiment indicated that the method has not only better distinct edge but also has faster speed.There are many methods that based on image characteristics is the most common method in image registration. Based on the characteristics of the image registration, the characteristic features is mainly on point. In order to obtain a registration faster high registration rate algorithm, the feature points Registration is applied in this paper. Firstly, image feature points are extracted by using SUSAN operator, secondly, the best matching parameters are searched in the search space by using of the PSO, and finally, the image registration is operated. In SUSAN operator, value of the t (that is gray level difference threshold) decide the smallest contrast and the ability to remove noise that SUSAN operator can detected, we improvement the value of the t , give a adaptive extraction methods that to the value of the t . PSO is a new parallel optimization algorithm, it can solve large nonlinear, non-differentiable, non-continuous and multi-complex problem, but the PSO easy fall into the local optimal, that is premature convergence phenomenon. In order to overcome the shortcomings of PSO algorithm, we propose that improving the PSO algorithm with Alopex the algorithm, it will be conducive to PSO algorithm jumping out of the local optimal in searching, at the same time accelerating the convergence of algorithm in accordance with the changes of the objective function. Finally, we verify the algorithm with an infrared image, a microwave image and a multi-spectral image, and compare with ICP and improving before the PSO algorithm in step, time and accuracy of the algorithm, the experimental results indicated that the method can effectively image registration.