Research On Space-Variant PSF Image Restoration Method In Computational Optical System

Computational optics systems are advantageous in simplifying structures,reducing costs,and improving imaging resolution while achieving miniaturization and weight reduction at the same time.However,it suffers from the problem of image blurring.Most restoration methods are based on space-invariant point spread function(PSF,Point Spread Function).However,affected by aberrations,the PSF of computational optical systems often changes with the field of view.In addition,the existing non-blind deconvolution of space-variant PSF images mainly adopts the method of block restoration,which is prone to an obvious ringing effect after splicing,and the restoration effect on optically blurred images is very limited.In this thesis,a space-variant PSF image restoration technology based on a simple computational optical system is proposed,which combines optical design and digital image processing to restore high-quality images.An improved sectioned restoration method is proposed to weaken the ringing effect of sub-image stitching by studying the space-variant PSF image restoration of a simple computational optical system.In addition,to overcome the problem that the classical filtering algorithm cannot effectively preserve the image edge information,a Total Variation(TV,Total Variation)regularized space-variant PSF image restoration algorithm is proposed to restore higher-quality images.The major contents of this thesis include the following parts:(1)This thesis proposes a sectioned restoration method of space-variant PSF images that analyzes both subjectively and objectively,in which overlapping region pixels and overlapping region weight coefficients are introduced.The optimal number of sections for the output blurred image of the simple calculation optical system is determined from the subjective and objective levels.Using the Wiener filter and Richardson-Lucy algorithms,the space-variant PSF image sectioned restoration is performed on the blurred image of the simple computational optical system.The results demonstrate that the proposed sectioned restoration method can effectively attenuate the ringing effect caused by sub-image stitching.(2)This thesis proposes a TV-regularized space-variant PSF image restoration algorithm.Firstly,the column-wise decomposition of the space-variant PSF is performed,then two auxiliary variables are introduced through the TV regularization model to transform the constrained optimization problem into a minimization problem,and finally the alternating direction method of multiplier(ADMM,Alternating Direction Method of Multipliers)is used to achieve the iterative solution of the restored image.The effectiveness of the algorithm is demonstrated by analyzing and comparing the restored results of different sizes of blurred images output from the computational optical system.In this thesis,the restoration of space-variant PSF blurred images output by computational optical systems are investigated.The space-variant PSF image sectioned restoration method effectively attenuates the ringing effect of image stitching by introducing the overlapping region pixels and overlapping region weight coefficients.In addition,the TV-regularized space-variant PSF image restoration algorithm improves the peak signal-to-noise ratio of the simple lens by about 1.4% and the sharpness by about 10%,which can significantly improve the image sharpness while maintaining image edge information well.The method in this thesis lays the foundation for high-resolution imaging of simple computational optical systems.In the future,computational optical system with low cost,lightweight,and high imaging quality will play a great potential in the fields of security and social life monitoring in key areas.