Abstract
This paper proposes a general framework for internal patch-based image restoration based on Conditional Random Fields (CRF). Unlike related models based on Markov Random Fields (MRF), our approach explicitly formulates the posterior distribution for the entire image. The potential functions are taken as proportional to the product of a likelihood and prior for each patch. By assuming identical parameters for similar patches, our approach can be classified as a model-based non-local method. For the prior term in the potential function of the CRF model, multivariate Gaussians and multivariate scale-mixture of Gaussians are considered, with the latter being a novel prior for image patches. Our results show that the proposed approach outperforms methods based on Gaussian mixture models for image denoising and state-of-the-art methods for image interpolation/inpainting.
Abstract (translated)
本文提出了一种基于条件随机场（CRF）的基于内部补丁的图像恢复的通用框架。与基于马尔可夫随机场（MRF）的相关模型不同，我们的方法明确地规定了整个图像的后验分布。潜在的功能与每个补丁的可能性和先验的乘积成比例。通过假设相似补丁的相同参数，我们的方法可以归类为基于模型的非本地方法。对于CRF模型的潜在函数中的先前项，考虑多元高斯和多元高斯尺度混合，后者是图像块的新颖先验。我们的结果表明，所提出的方法优于基于高斯混合模型的图像去噪方法和用于图像插值/修复的最先进方法。
URL
https://arxiv.org/abs/1807.03027
