In this paper, we propose a unified framework of denoising score-based models in the context of graduated non-convex energy minimization. We show that for sufficiently large noise variance, the associated negative log density -- the energy -- becomes convex. Consequently, denoising score-based models essentially follow a graduated non-convexity heuristic. We apply this framework to learning generalized Fields of Experts image priors that approximate the joint density of noisy images and their associated variances. These priors can be easily incorporated into existing optimization algorithms for solving inverse problems and naturally implement a fast and robust graduated non-convexity mechanism.

翻译：本文提出一种在渐进非凸能量最小化框架下的去噪分数模型统一框架。研究表明，当噪声方差足够大时，对应的负对数密度（即能量函数）呈现凸性。因此，去噪分数模型本质上遵循渐进非凸性启发式策略。我们将该框架应用于学习广义专家场图像先验，该先验可逼近含噪图像及其对应方差的联合分布。这类先验能便捷地整合到现有逆问题求解优化算法中，并自然实现快速鲁棒的渐进非凸机制。