Research On UAV Remote Sensing Blurred Image Restoration Technology

UAV remote sensing imaging can obtain the information of economic,fast,safe,and so on resources exploration,environment monitoring,the battlefield reconnaissance,and other fields has a high application value.When because of the unmanned aerial vehicle is affected by the bad weather and its tilt shaking,captured images with complex noise,blurred,low contrast,details of the texture is not clear wait for a characteristic,make the image quality is greatly reduced.In order to obtain clear image information,the most of the image restoration algorithm is based on the blurred image under the premise of known point spread function(PSF),such as inverse filter,wiener filtering,etc.,but the reality of imaging condition is complicated.Aviation camera irregular vibration,and by relative motion between the target and atmospheric turbulence are unknown,cannot know the precise blurred kernel function,so the research of blind restoration algorithm,has the dual demand of reality and theory.Paper disturbance in camera noise and outliers,motion blur,atmospheric transmission blur degraded image restoration problem,respectively established optimization function model and design the corresponding recovery method,to remove the image degradation phenomenon,improve the quality of image and keep the details of the image as the main entry point to study the influence factors,such as a new method of UAV remote sensing fuzzy image restoration,its main content is as follows:For UAV aerial image motion blur problem,puts forward a UAV remote sensing image restoration algorithm based on L0 sparse prior.First,by analyzing the characteristics of the image,we can get the inherent property that the gradient distribution of the blurred image is denser than the clear image and the sparse of the dark channel is relatively small.Then aiming at the L0 norm is highly non-convex and the optimization involves a non-linear minimum operation,we propose an approximate linear map matrix based on look-up tables,and solve the linearized L0 minimization problem by half-quadratic splitting methods.Finally,the fast Fourier transform is used to alternate the fuzzy kernel and the clear image to output the restored image.Through experiments on several different types of blurred images,The image quality objective evaluation indexes were significantly increased.It can effectively suppress the ringing effect near the edge of the image,retain the integrity of clear details and significantly improve the speed of operation,is suitable for all kinds of image restoration.Camera outliers and non-gaussian noise seriously affects the correct estimation of blur nuclear,poor performance of the traditional image restoration algorithm,details lost badly,obvious effect of the artificial.This paper proposed a saturated blurred image blind restoration algorithm with removing camera outliers.First,the L1 regularization model is established according to the gray characteristics of the saturated image,and a hyper-Laplace prior is used to extract the salient edges of the image.Then aiming at the S function can not completely filter the saturated pixels in the edge,we propose a blur kernel auxiliary function which can effectively eliminate outliers by setting threshold.Finally,by analyzing the influence of outliers of blur kernel estimating,we establish blind deconvolution model based on outlier-aware.Aiming at quadratic problem,the iterative weighted least squares method is used to obtain the restored image.Through experiments on several different saturated blurred images,the results show that the algorithm can minimize the influence of the outliers,the camera correctly estimate the blur kernel function,keep the details of clear and significantly improves the operation speed,superior to today's most advanced image blind restoration algorithm.Finally for unmanned aerial remote sensing images affected by atmospheric disturbances in the process of obtaining the atmospheric blur qualitative problems,puts forward a kind of image to blur algorithm based on the atmospheric transmission characteristics.The method is based on the analysis of the atmospheric physical characteristics of light scattering and absorption,build the atmosphere transmission point spread function estimation model,and design and the new algorithm of the model matching,designed to remove the UAV remote sensing atmospheric turbulence degraded image blur,finish the class drop image restoration.Method by experimental simulation,this paper compared with other traditional algorithm,the quality of image restoration is more outstanding,and the noise has certain robustness.