Research On Super-Resolution Restoration Technique For Ground-Based Imaging Of Space Objects

With the development of astronomical detection technology,more and more people pay attention to the research of space object image.The pursuit of high quality image promotes the development of hardware technology,such as adaptive optics and image restoration technology.Image restoration algorithm is an image post-processing technology aiming to improve the image quality and enrich the details of space objects,which is observed by ground-based optical telescopes.The usage of image restoration has a great significance in astronomical observation,target recognition,space exploration,military reconnaissance and other fields.However,the resolution(pixel count)of the restored image has not been improved because of the poor quality of the original observed image.Based on the characteristics of high temporal resolution of continuous images,useful information in low resolution images can be further excavated,and information beyond the cut-off frequency of the detector can be recovered.Based on the previous studies on the traditional image restoration technology and super-resolution reconstruction technology,several improvements have been accomplished as follows:(1)A super-resolution restoration algorithm for spatial objects is proposed,which combines the advantages of blind deconvolution deblurring and super-resolution reconstruction to eliminate frequency-domain aliasing,moreover,the quality of restored images is further improved.(2)An iterative reweighted blind deconvolution algorithm is proposed.In order to improve the robustness of the restoration algorithm,a weight matrix is introduced to solve the problem that the imaging process of some images does not strictly follow the assumed imaging model.(3)An image enhancement algorithm is proposed.According to the characteristics of large dynamic range of image’s pixel brightness value,the image enhanced by the algorithm is convenient for human eyes or detectors to recognize and detect and highlight useful information of the image.