IEEE Trans Pattern Anal Mach Intell. 2017 Aug;39(8):1518-1531. doi: 10.1109/TPAMI.2016.2604816. Epub 2016 Aug 31.
Most existing image denoising approaches assumed the noise to be homogeneous white Gaussian distributed with known intensity. However, in real noisy images, the noise models are usually unknown beforehand and can be much more complex. This paper addresses this problem and proposes a novel blind image denoising algorithm to recover the clean image from noisy one with the unknown noise model. To model the empirical noise of an image, our method introduces the mixture of Gaussian distribution, which is flexible enough to approximate different continuous distributions. The problem of blind image denoising is reformulated as a learning problem. The procedure is to first build a two-layer structural model for noisy patches and consider the clean ones as latent variable. To control the complexity of the noisy patch model, this work proposes a novel Bayesian nonparametric prior called "Dependent Dirichlet Process Tree" to build the model. Then, this study derives a variational inference algorithm to estimate model parameters and recover clean patches. We apply our method on synthesis and real noisy images with different noise models. Comparing with previous approaches, ours achieves better performance. The experimental results indicate the efficiency of the proposed algorithm to cope with practical image denoising tasks.
大多数现有的图像去噪方法都假设噪声是均匀的白色高斯分布，且噪声强度已知。然而，在实际的噪声图像中，噪声模型通常是未知的，并且可能更加复杂。本文针对这一问题，提出了一种新的盲图像去噪算法，能够从具有未知噪声模型的噪声图像中恢复出干净的图像。为了对图像的经验噪声进行建模，我们的方法引入了混合高斯分布，它具有足够的灵活性，可以逼近不同的连续分布。盲图像去噪问题被重新表述为一个学习问题。该过程首先为噪声块构建一个两层结构模型，并将干净的块视为潜在变量。为了控制噪声块模型的复杂性，这项工作提出了一种新的贝叶斯非参数先验，称为“依赖狄利克雷过程树”，用于构建模型。然后，我们推导了一种变分推理算法来估计模型参数并恢复干净的块。我们将该方法应用于具有不同噪声模型的合成和真实噪声图像。与之前的方法相比，我们的方法取得了更好的性能。实验结果表明，该算法在处理实际图像去噪任务时具有很高的效率。
