Blind deconvolution is a common method for restoration of blurred text images, while binarization is employed to analyze and interpret the text semantics. In literature, these tasks are typically treated independently. This paper introduces a novel binarization driven blind deconvolution approach to couple both tasks in a common framework. The proposed method is derived as an energy minimization problem regularized by a novel consistency term to exploit text binarization as a prior for blind deconvolution. The binarization to establish our consistency term is inferred by spatially regularized soft clustering based on a set of discriminative features. Our algorithm is formulated by the alternating direction method of multipliers and iteratively refines blind deconvolution and binarization. In our experimental evaluation, we show that our joint framework is superior to treating binarization and deconvolution as independent subproblems. We also demonstrate the application of our method for the restoration and binarization of historic document images, where it is comparable to state-of-the-art algorithms.

Supplementary material:

• The presentation slides for this work are available here.