Abstract. Natural images inevitably suffer from spatially variant blur caused by the relative motion between a camera and objects. We present an effective and efficient patch-wise edge-enhanced image regularization and a robust kernel similarity constraint to perform an accurate kernel estimation from coarse-to-fine iterations. The proposed adaptive regularization introduces a gradient magnitude penalty function into total variation to preserve and enhance salient edges while smoothing out harmful subtle structures. In addition, the similarity constraint is engaged in each patch without camera rotation effects, ensuring that the erroneous kernels can be identified by measuring the similarity among the kernels of neighbor patches and be replaced with the well-estimated ones. After obtaining accurate kernels, numerous nonblind deblurring methods can be applied to restore an image. Numerical experiments demonstrate that the proposed algorithm performs favorably without ringing artifacts and possesses high processing efficiency for natural nonuniform blurred images.

中文翻译：

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基于自适应边缘增强正则化的单幅图像非均匀盲去模糊

摘要。自然图像不可避免地会受到由相机和物体之间的相对运动引起的空间变化模糊的影响。我们提出了一种有效且高效的逐块边缘增强图像正则化和稳健的内核相似性约束，以执行从粗到精迭代的准确内核估计。所提出的自适应正则化将梯度幅度惩罚函数引入到总变化中，以保留和增强显着边缘，同时消除有害的细微结构。此外，在没有相机旋转影响的每个补丁中进行相似性约束，确保可以通过测量相邻补丁内核之间的相似性来识别错误内核，并用估计好的内核替换。获得准确的内核后，许多非盲去模糊方法可用于恢复图像。数值实验表明，该算法性能良好，无振铃伪影，对自然非均匀模糊图像具有较高的处理效率。