IEEE Trans Image Process. 2015 Dec;24(12):5469-78. doi: 10.1109/TIP.2015.2473098. Epub 2015 Aug 25.
Image priors are essential to many image restoration applications, including denoising, deblurring, and inpainting. Existing methods use either priors from the given image (internal) or priors from a separate collection of images (external). We find through statistical analysis that unifying the internal and external patch priors may yield a better patch prior. We propose a novel prior learning algorithm that combines the strength of both internal and external priors. In particular, we first learn a generic Gaussian mixture model from a collection of training images and then adapt the model to the given image by simultaneously adding additional components and refining the component parameters. We apply this image-specific prior to image denoising. The experimental results show that our approach yields better or competitive denoising results in terms of both the peak signal-to-noise ratio and structural similarity.
图像先验对于许多图像恢复应用至关重要，包括去噪、去模糊和修复。现有的方法要么使用来自给定图像的先验（内部），要么使用来自单独图像集合的先验（外部）。我们通过统计分析发现，统一内部和外部补丁先验可能会产生更好的补丁先验。我们提出了一种新的先验学习算法，该算法结合了内部和外部先验的优势。具体来说，我们首先从一组训练图像中学习一个通用的高斯混合模型，然后通过同时添加额外的分量和细化分量参数，将模型适应当前图像。我们将这种特定于图像的先验应用于图像去噪。实验结果表明，我们的方法在峰值信噪比和结构相似性方面都能获得更好或有竞争力的去噪结果。
