Robust reconstruction of high resolution grayscale images from a sequence of low-resolution frames (robust gray super-resolution)

Abstract

A method for computing a high resolution gray-tone image from a sequence of low-resolution images uses an L 1 norm minimization. In a preferred embodiment, the technique also uses a robust regularization based on a bilateral prior to deal with different data and noise models. This robust super-resolution technique uses the L 1 norm both for the regularization and the data fusion terms. Whereas the former is responsible for edge preservation, the latter seeks robustness with respect to motion error, blur, outliers, and other kinds of errors not explicitly modeled in the fused images. This computationally inexpensive method is resilient against errors in motion and blur estimation, resulting in images with sharp edges. The method also reduces the effects of aliasing, noise and compression artifacts. The method's performance is superior to other super-resolution methods and has fast convergence.

1 . A computer-implemented method for super-resolution, the method comprising: 
 computing a super-resolved image from a plurality of lower-resolution images using a maximum liklihood estimator based on an L 1  norm minimization.    
     
     
         2 . The method of  claim 1  wherein the computing further comprises using a bilateral total variation regularization term.