Abstract
Blind image deconvolution is a typically ill-conditioned inverse problem that requires additional information to constrain the solution space. The purpose of this paper is to investigate the characteristics of generalized lp/lq norm on the derivatives of nature images and present a novel efficient method for blind image deconvolution with generalized lp/lq norm (0 < p ≤ 1, p < q). Firstly we analyze the mathematical characteristics of generalized lp/lq norm base on the BSDS dataset, then apply generalized lp/lq norm-based prior model in gradient space to estimate the blur kernel. Due to the complexity of optimization model, we develop an alternating gradient descent method to solve the generalized lp/lq norm-based model which can achieve high recovery quality. Specifically, the selection strategy of regularization parameters is given by using generalized cross-validation method, and these parameters can be updated in alternating minimization steps. Our preliminary experiments show that the proposed method can achieve state-of-the-art results.
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