IEEE Trans Pattern Anal Mach Intell. 2016 Apr;38(4):677-89. doi: 10.1109/TPAMI.2015.2441053.
Conditional random fields (CRFs) are popular discriminative models for computer vision and have been successfully applied in the domain of image restoration, especially to image denoising. For image deblurring, however, discriminative approaches have been mostly lacking. We posit two reasons for this: First, the blur kernel is often only known at test time, requiring any discriminative approach to cope with considerable variability. Second, given this variability it is quite difficult to construct suitable features for discriminative prediction. To address these challenges we first show a connection between common half-quadratic inference for generative image priors and Gaussian CRFs. Based on this analysis, we then propose a cascade model for image restoration that consists of a Gaussian CRF at each stage. Each stage of our cascade is semi-parametric, i.e., it depends on the instance-specific parameters of the restoration problem, such as the blur kernel. We train our model by loss minimization with synthetically generated training data. Our experiments show that when applied to non-blind image deblurring, the proposed approach is efficient and yields state-of-the-art restoration quality on images corrupted with synthetic and real blur. Moreover, we demonstrate its suitability for image denoising, where we achieve competitive results for grayscale and color images.
条件随机场（CRFs）是计算机视觉中流行的判别模型，已成功应用于图像恢复领域，特别是图像去噪。然而，对于图像去模糊，判别方法大多缺乏。我们认为有两个原因：首先，模糊核通常只在测试时知道，这要求任何判别方法都要应对相当大的可变性。其次，考虑到这种可变性，为判别预测构建合适的特征是相当困难的。为了解决这些挑战，我们首先展示了常见的半二次生成图像先验推断和高斯 CRF 之间的联系。基于此分析，我们随后提出了一种用于图像恢复的级联模型，该模型在每个阶段都包含一个高斯 CRF。我们级联的每个阶段都是半参数的，即它取决于恢复问题的特定于实例的参数，例如模糊核。我们通过使用合成生成的训练数据进行损失最小化来训练我们的模型。我们的实验表明，当应用于非盲图像去模糊时，所提出的方法是高效的，并在合成和真实模糊的图像上获得了最先进的恢复质量。此外，我们证明了它适用于图像去噪，在灰度和彩色图像中我们取得了有竞争力的结果。
