IEEE Trans Image Process. 2016 Jul;25(7):3426-3437. doi: 10.1109/TIP.2016.2571062. Epub 2016 May 19.
Low-rank matrix approximation has been successfully applied to numerous vision problems in recent years. In this paper, we propose a novel low-rank prior for blind image deblurring. Our key observation is that directly applying a simple low-rank model to a blurry input image significantly reduces the blur even without using any kernel information, while preserving important edge information. The same model can be used to reduce blur in the gradient map of a blurry input. Based on these properties, we introduce an enhanced prior for image deblurring by combining the low rank prior of similar patches from both the blurry image and its gradient map. We employ a weighted nuclear norm minimization method to further enhance the effectiveness of low-rank prior for image deblurring, by retaining the dominant edges and eliminating fine texture and slight edges in intermediate images, allowing for better kernel estimation. In addition, we evaluate the proposed enhanced low-rank prior for both the uniform and the non-uniform deblurring. Quantitative and qualitative experimental evaluations demonstrate that the proposed algorithm performs favorably against the state-of-the-art deblurring methods.
近年来，低秩矩阵逼近已成功应用于众多视觉问题。在本文中，我们提出了一种用于盲图像去模糊的新型低秩先验。我们的关键观察结果是，即使不使用任何核信息，直接将简单的低秩模型应用于模糊输入图像也能显著减少模糊，同时保留重要的边缘信息。相同的模型可用于减少模糊输入梯度图中的模糊。基于这些特性，我们通过结合模糊图像及其梯度图中相似块的低秩先验，引入了一种增强的图像去模糊先验。我们采用加权核范数最小化方法，通过保留主导边缘并消除中间图像中的精细纹理和轻微边缘，进一步提高低秩先验在图像去模糊中的有效性，从而实现更好的核估计。此外，我们对所提出的增强低秩先验在均匀和非均匀去模糊方面进行了评估。定量和定性实验评估表明，所提出的算法优于当前的去模糊方法。
