Binary image deblurring with automatic binary value estimation

Xiao-Guang Lv, Fang Li

Abstract: We propose a total variation-based variational model for nonblind binary image deblurring. The binary constraint is considered using the double-well function as the penalty term. We show the existence of a minimizer for the proposed model. By using operator splitting and alternating split Bregman, we get an effective numerical algorithm for the proposed model. Different from the existing methods in which the binary values are assumed to be known, our method can estimate the binary values automatically in the iteration process. Numerical results and comparisons demonstrate that the proposed algorithm is promising.