论文标题
通过稀疏表示，OCT图像的非凸线超分辨率
Non-convex Super-resolution of OCT images via sparse representation
论文作者
论文摘要
我们通过对从高分辨率OCT数据中学到的合适词典来实施稀疏性，提出了一个非凸变量模型，用于鼠眼的光学相干断层扫描（OCT）图像的超分辨率。 OCT图像的统计特征通过考虑非高斯病例α= 1来激发α-稳定分布在学习词典中的使用。促进稀疏成本功能依赖于非凸惩罚 - 基于库奇的或minimax凹惩罚（MCP） - 这使得问题特别具有挑战性。我们提出了一种有效的算法，以最大程度地限制基于前进的分裂策略，以确保每次迭代时保证近端点的存在和独特性。与基于标准凸L1的重建的比较表明，非凸模型的性能更好，尤其是鉴于进一步的OCT图像分析
We propose a non-convex variational model for the super-resolution of Optical Coherence Tomography (OCT) images of the murine eye, by enforcing sparsity with respect to suitable dictionaries learnt from high-resolution OCT data. The statistical characteristics of OCT images motivate the use of α-stable distributions for learning dictionaries, by considering the non-Gaussian case, α=1. The sparsity-promoting cost function relies on a non-convex penalty - Cauchy-based or Minimax Concave Penalty (MCP) - which makes the problem particularly challenging. We propose an efficient algorithm for minimizing the function based on the forward-backward splitting strategy which guarantees at each iteration the existence and uniqueness of the proximal point. Comparisons with standard convex L1-based reconstructions show the better performance of non-convex models, especially in view of further OCT image analysis
