Blind Image Super-resolution Reconstruction Based on Double Regularization
基于双正则化的图像超分辨率盲重建

Image super-resolution reconstruction(SRR)refers to a signal processing approach which produces high-resolution images from observed multiple low-resolution images.Many image SRR algorithms assume that the blurring process,i.e.,point spread function(PSF)of the imaging system is known prior to reconstruction.However,the blurring process is not known or is known only to within a set of parameters in many practical applications.In this paper,we propose an approach for blind image SRR based on double regularization by parametrizing PSF.A space-adaptive regularization method for image SRR is used to preserve detail at the textured regions and suppress noise in the smooth background.In the scheme,PSF parameter(s)and the high-resolution image are estimated by alternating minimization method.The demand for precision of minimizations is varied during the optimization procedure in order to reduce the computation cost.Experimental results from a synthetic image sequence show that blur parameters are approximated actually and the reconstructed image is visually pleasing.