Research On Image Restoration For Sparse-aperture Imaging

In high-altitude reconnaissance or remote sensing detection,the imaging system is far away from the target,which will be photographed.In order to obtain more information about the target that be photographed,the imaging system is usually required to have extremely high resolution.To obtain higher imaging resolution,the aperture of the optical system is required larger and larger.However,huge optical lenses have very strict processing requirements,and are easily susceptible to external environmental interference and cause precision errors.Therefore,the sparse aperture imaging system using multiple sub-apertures instead of a single large-aperture imaging system was designed.Compared with a single large-aperture imaging system,a sparse aperture imaging system has the advantages such as small size,stable performance,and low processing costs.However,due to the discretization of pupil distribution of sparse aperture optical system,the modulation transfer function decreases at midband spatial frequencies,and the image is visually blurred.Therefore,the blurred image needs to be restored by the image restoration algorithm.This article focuses on how to make the degraded image clear.Aiming at the problem that the degradation function of the sparse aperture imaging system is difficult to obtain through actual measurement,this article analyzes the cause of image blurring based on the imaging principle and degradation factors,and establishes a Golay-3 Sparse aperture imaging degradation model.First,establish a simplified imaging model of the sparse aperture imaging system.Derive the influence of various structural parameters on the optical function,and establish a model of the influence of the array structure on the imaging quality.Next,establish a model of the influence of atmospheric turbulence on the imaging quality during the transmission process.Then,based on the possible errors in the assembly process,establish a model for the influence of optical errors on imaging quality.Finally,Synthesize three kinds of influencing factors,restore the process of image degradation,and calculate the degradation function that is sufficient in the process of image degradation.Based on the calculated degradation function to restore image.Aiming at the existing image restoration algorithms with poor restoration effect and difficult adaptation,this article established a restoration algorithm based on improved Wiener filter and optimization of adjacent frames.This algorithm can adaptively gradually optimize the quality of the restored image.First of all,a SABL sharpness evaluation factor is improved,which makes the calculation easier while ensuring the effect.Then,in order to solve the problem that the Wiener filter has a high dependency on the prior image,the Wiener filter is improved by adding an adaptive Gaussian window,which makes Wiener filtering is more universal.Finally,a frame-by-frame optimization algorithm is proposed for the problem of difficulty in adapting the restoration process.According to the association between the upper and lower frames in the captured video,the quality of the restored image is gradually optimized and stabilized at a higher definition.For the image restoration algorithm involved,a simulation experiment was carried out under the environment of Matlab 2014 a.First,simulated a set of degraded images according to the application environment.Then use the algorithm in this paper and several existing image restoration algorithms to restore the degraded image.Comparing the quality of the restored image,it is easily to find that the algorithm in this paper has better restoration effect and better feasibility.