Pan Jinshan, Sun Deqing, Pfister Hanspeter, Yang Ming-Hsuan
IEEE Trans Pattern Anal Mach Intell. 2018 Oct;40(10):2315-2328. doi: 10.1109/TPAMI.2017.2753804. Epub 2017 Sep 22.
We present an effective blind image deblurring algorithm based on the dark channel prior. The motivation of this work is an interesting observation that the dark channel of blurred images is less sparse. While most patches in a clean image contain some dark pixels, this is not the case when they are averaged with neighboring ones by motion blur. This change in sparsity of the dark channel pixels is an inherent property of the motion blur process, which we prove mathematically and validate using image data. Enforcing sparsity of the dark channel thus helps blind deblurring in various scenarios such as natural, face, text, and low-illumination images. However, imposing sparsity of the dark channel introduces a non-convex non-linear optimization problem. In this work, we introduce a linear approximation to address this issue. Extensive experiments demonstrate that the proposed deblurring algorithm achieves the state-of-the-art results on natural images and performs favorably against methods designed for specific scenarios. In addition, we show that the proposed method can be applied to image dehazing.
我们提出了一种基于暗通道先验的有效盲图像去模糊算法。这项工作的动机源于一个有趣的观察结果，即模糊图像的暗通道稀疏性较低。虽然干净图像中的大多数小块包含一些暗像素，但当它们通过运动模糊与相邻像素进行平均时，情况并非如此。暗通道像素稀疏性的这种变化是运动模糊过程的固有属性，我们通过数学证明并使用图像数据进行了验证。因此，强制暗通道的稀疏性有助于在各种场景（如自然图像、面部图像、文本图像和低光照图像）中进行盲去模糊。然而，施加暗通道的稀疏性会引入一个非凸非线性优化问题。在这项工作中，我们引入了一种线性近似来解决这个问题。大量实验表明，所提出的去模糊算法在自然图像上取得了当前最优的结果，并且在与针对特定场景设计的方法相比时表现良好。此外，我们表明所提出的方法可以应用于图像去雾。
