Remote Sensing Image Degradation Mechanism And The Research On The Key Technology Of Recovery

There are many steps in forming remote sensing images, and inevitably the degradation appears, and so as the quality of the image. In order to obtain a higher quality of space image, it is necessary to remove the noise and eliminate the ambiguity. Generally, there are two methods:image enhancement and image restoration.Nowadays, various agencies at home and abroad do many searches on remote sensing image to obtain high quality. As a result, many restore method come out. Such as the image restore based on point spread function, image restore based on modulation transfer function compensation. This paper study the process of getting the point spread function using knife edge method, and the influence factors on the restoration effect to image analysis. And put forward the evaluation system for the evaluation of remote sensing image restoration effect.The main works of this paper are as follows:(1) The accuracy of image for data fitting the point spread function of time, this paper proposes an optimal Fermi function fitting scheme evaluation based on image edge spread function fitting.(2) The restoration algorithm is verified by the restoration effect, and explores the restoration effect recovery Lucy-Richardson recovery method in times of remote sensing image.(3) For the image evaluation result, put forward a set of scheme evaluation based on image noise for image evaluation after recovery after restoration, the experiment proved the effectiveness of the proposed scheme.(4) Studying the evaluation algorithm for image evaluation, efficiency, is put forward to evaluate the selected area effect instead of the whole image evaluation scheme based on the scheme, in the experiment has also been validated.(5) Summary of image current evaluation method, using the efficient algorithm of image evaluation, combined with the extreme seeking method in mathematical modeling, a set of evaluation system for the evaluation of the restored image is proposed. Experiments show the effectiveness of the scheme for remote sensing image evaluation.(6) Using VC and Matlab mixed programming to verify the experiment process, and by using VC multi thread programming to verify the image restoration effect.