We present an effective blind image deblurring method based on the reweighted L1 norm prior. The prior is motivated by that the traditional L1 norm highly depends on the pixel itself, that is, the larger the pixel value, the greater the penalty. However, the blur reduces the high-frequency components of the clear image, and minimizing the high-frequency part will result in a blur solution or delta function kernel. To overcome this limitation, we employ the reweighted L1 norm and it eliminates this dependence within wisely weighting. The image prior compensates for the degeneration of high intensities and greatly stabilizes the intermediate image estimation process. However, the prior proposed introduces a challenging optimization problem. We develop an efficient optimization scheme to obtain a reliable intermediate image for estimating the blur kernel. Extensive experiments on different kinds of challenging blurry images demonstrate the superiority of our proposed method over the state-of-the-art blind deblurring methods. Moreover, our blind deblurring algorithm is effective in various scenarios, such as natural, text, and low-light images.

中文翻译：

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通过增强的稀疏先验实现盲图像去模糊

我们提出了一种基于重新加权的L1范数的有效盲图像去模糊方法。先验的动机是传统的L1范数高度依赖于像素本身，也就是说，像素值越大，惩罚越大。但是，模糊会减少清晰图像的高频分量，而最小化高频部分将导致模糊解或增量函数核。为了克服此限制，我们采用了重新加权的L1范数，它在明智的加权范围内消除了这种依赖性。图像先验补偿了高强度的退化，并极大地稳定了中间图像估计过程。然而，先前提出的方案引入了具有挑战性的优化问题。我们开发了一种有效的优化方案，以获得可靠的中间图像来估计模糊核。在不同类型的具有挑战性的模糊图像上进行的大量实验证明，我们提出的方法优于最新的盲去模糊方法。而且，我们的盲去模糊算法在各种情况下都有效，例如自然，文本和低光图像。