Abstract
The goal of blind image deblurring is to estimate the blur kernel and restore the sharp latent image based on an input blur image. This paper proposes a novel blind image deblurring algorithm based on L0-regularization and kernel shape optimization. Firstly, the proposed objective function of the optimization model is formulated with L0-norm terms of the gradient and intensity of kernels, which results to good sparsity and less noise in the obtained kernel. Then, the coarse-to-fine iterative framework is adopted to estimate reliable salient image structures implicitly, which can reduce computation and accelerate convergence. Finally, the kernel shape is optimized by weighting method, which enables the obtained kernel closer to the ground-truth. Experimental results on public bench mark datasets demonstrate that restored images are clear with less ring-artifacts.
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