earticle

영어

This paper models image deconvolution as an l2-l1 minimization problem, which is an approach taken by many state-of-the-art image deconvolution algorithms. We present a novel iterative algorithm based on the split Bregman method and the stationary second-degree method, which efficiently addresses the classic convex minimization problem. The split Bregman method, which has been proven to be very efficient for non-differentiable minimization problems, decomposes the equivalent constrained version of the l2-l1 deconvolution problem into a series of sub-problems. These sub-problems are then individually solved using appropriate methods to obtain their closed-form solutions. Unlike the majority of other similar deconvolution algorithms, we use a modified stationary second-degree method to solve the l2-l1 denoising sub-problem, prompted by some recent work on the improvement of the iterative thresholding method. The presented algorithm can be categorized as a split Bregman method, so convergence of the solution can be guaranteed. In our experiment, the presented algorithm and the algorithms in references [6] and [8] are used to restore Gaussian-blurry and uniform-blurry images. The experimental results show that the presented algorithm is effective and it outperforms other algorithms in comparison.