Abstract Blind image deblurring is a very challenging inverse problem due to the severe ill-posedness caused by the unknown kernel and the latent clear image. To tackle this problem, appropriate smoothing regularizations and image priors are usually employed and incorporated into the associated variational models to alleviate the inherent ill-posedness. In this paper, we first propose a strongly imposed zero patch minimum constraint for the latent image, which helps alleviate the ill-posedness of the inverse problem for blind image deblurring. Then, we retrieve important fine details by assigning the patch minimum information obtained from the blurred image back to the latent image to further enhance its structure. Finally, we introduce an adaptive regularizer which was shown to have significantly better edge-preserving property than the total variation regularizer for the image restoration of degraded images. Operator splitting techniques are used to accomplish an efficient numerical implementation of the proposed variational model. A number of numerical experiments and comparisons with some state-of-the-art methods are conducted to demonstrate the effective performance of the newly proposed method.

中文翻译：

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基于补丁最小信息稀疏性的盲图像去模糊

摘要 由于未知核和潜在清晰图像引起的严重不适定性，盲图像去模糊是一个非常具有挑战性的逆问题。为了解决这个问题，通常采用适当的平滑正则化和图像先验并将其合并到相关的变分模型中以减轻固有的不适定性。在本文中，我们首先为潜在图像提出了一个强强加的零补丁最小约束，这有助于缓解盲图像去模糊的逆问题的不适定性。然后，我们通过将从模糊图像获得的补丁最小信息分配回潜在图像以进一步增强其结构来检索重要的细节。最后，我们引入了一种自适应正则化器，它被证明具有比总变化正则化器更好的边缘保留特性，用于退化图像的图像恢复。算子分裂技术用于实现所提出的变分模型的有效数值实现。进行了大量数值实验并与一些最先进的方法进行了比较，以证明新提出的方法的有效性能。