A fuzzy blur algorithm to adaptive blind image deconvolution

This paper proposes a new approach to blind image deconvolution based on fuzzy blur interference algorithm. Conventional blind algorithms require a crisp decision to be made on the structure of the blurring function prior to formulation. This creates a dilemma as the complete blur information is usually unknown a priori. Most blind algorithms either employ absolute but inflexible parametric modeling or ignore the parametric blur knowledge completely. This paper presents a fuzzy approach to resolve this difficulty by constructing a soft model set consisting of parametric estimates of the current blur. The relevance of these estimates are evaluated, and integrated to form a fuzzy blur using compositional inference rule. The main feature of the technique lies in its ability to incorporate domain knowledge while preserving the flexibility of the scheme. Experimental results show that the technique is effective in restoring blurred, noisy images without prior knowledge of the blur.