We present an effective blind image deblurring method based on a data-driven discriminative prior. Our work is motivated by the fact that a good image prior should favor sharp images over blurred ones. In this work, we formulate the image prior as a binary classifier using a deep convolutional neural network. The learned prior is able to distinguish whether an input image is sharp or not. Embedded into the maximum a posterior framework, it helps blind deblurring in various scenarios, including natural, face, text, and low-illumination images, as well as non-uniform deblurring. However, it is difficult to optimize the deblurring method with the learned image prior as it involves a non-linear neural network. In this work, we develop an efficient numerical approach based on the half-quadratic splitting method and gradient descent algorithm to optimize the proposed model. Furthermore, we extend the proposed model to handle image dehazing. Both qualitative and quantitative experimental results show that our method performs favorably against the state-of-the-art algorithms as well as domain-specific image deblurring approaches.

中文翻译：

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通过深度判别先验的盲图像去模糊

我们提出了一种基于数据驱动判别性先验的有效盲图像去模糊方法。我们的工作受到这样一个事实的推动，即良好的图像先验应该有利于清晰图像而不是模糊图像。在这项工作中，我们使用深度卷积神经网络将图像先验公式化为二元分类器。学习到的先验能够区分输入图像是否清晰。嵌入到最大后部框架中，有助于在各种场景中进行盲去模糊，包括自然、人脸、文本和低照度图像，以及非均匀去模糊。然而，由于它涉及非线性神经网络，因此很难用学习的图像先验优化去模糊方法。在这项工作中，我们开发了一种基于半二次分裂方法和梯度下降算法的有效数值方法来优化所提出的模型。此外，我们扩展了所提出的模型来处理图像去雾。定性和定量的实验结果都表明，我们的方法与最先进的算法以及特定领域的图像去模糊方法相比表现良好。