论文标题
贝叶斯模型选择无监督的图像反卷积与结构化高斯先验
Bayesian model selection for unsupervised image deconvolution with structured Gaussian priors
论文作者
论文摘要
本文考虑了随机模型的客观比较来解决反问题，更具体地说是图像恢复。大多数情况下，模型比较是以监督的方式解决的，这可能是耗时的，并且部分任意。在这里，我们采用了一种无监督的贝叶斯方法，并根据其后验概率客观地比较了这些模型，直接从没有可用地面真相的数据中直接从数据中进行比较。概率取决于模型的边际可能性或“证据”，我们采用包括Gibbs采样器在内的CHIB方法。我们专注于具有循环协方差和未知超参数的高斯模型家族，并比较了图像和噪声的不同类型的协方差矩阵。
This paper considers the objective comparison of stochastic models to solve inverse problems, more specifically image restoration. Most often, model comparison is addressed in a supervised manner, that can be time-consuming and partly arbitrary. Here we adopt an unsupervised Bayesian approach and objectively compare the models based on their posterior probabilities, directly from the data without ground truth available. The probabilities depend on the marginal likelihood or "evidence" of the models and we resort to the Chib approach including a Gibbs sampler. We focus on the family of Gaussian models with circulant covariances and unknown hyperparameters, and compare different types of covariance matrices for the image and noise.
