标题： 高光谱图像恢复约束于多粒度非局部自相似性先验 显示英文标题

标题： Hyperspectral Image Recovery Constrained by Multi-Granularity Non-Local Self-Similarity Priors

摘要： 高光谱图像（HSI）恢复作为上游图像处理任务，在分类、分割和检测等下游任务中具有重要意义。近年来，基于非局部先验表示的HSI恢复方法表现出色。然而，这些方法采用固定格式的因子来表示非局部自相似性张量组，使其无法适应多样的缺失场景。为了解决这个问题，我们首次引入张量分解中的粒度概念，并提出一种受多粒度非局部自相似性先验约束的HSI恢复模型。具体来说，所提出的模型对非局部自相似性张量组交替进行粗粒度分解和细粒度分解。其中，粗粒度分解基于Tucker张量分解，通过在模式展开矩阵上执行奇异值收缩来提取图像的全局结构信息。细粒度分解采用FCTN分解，通过建模因子张量之间的成对相关性来捕捉局部细节信息。这种架构方法实现了对HSI全局、局部和非局部先验的统一表示。实验结果表明，该模型具有强大的适用性，在像素和条纹等多种缺失场景中表现出色。 显示英文摘要

摘要： Hyperspectral image (HSI) recovery, as an upstream image processing task, holds significant importance for downstream tasks such as classification, segmentation, and detection. In recent years, HSI recovery methods based on non-local prior representations have demonstrated outstanding performance. However, these methods employ a fixed-format factor to represent the non-local self-similarity tensor groups, making them unable to adapt to diverse missing scenarios. To address this issue, we introduce the concept of granularity in tensor decomposition for the first time and propose an HSI recovery model constrained by multi-granularity non-local self-similarity priors. Specifically, the proposed model alternately performs coarse-grained decomposition and fine-grained decomposition on the non-local self-similarity tensor groups. Among them, the coarse-grained decomposition builds upon Tucker tensor decomposition, which extracts global structural information of the image by performing singular value shrinkage on the mode-unfolded matrices. The fine-grained decomposition employs the FCTN decomposition, capturing local detail information through modeling pairwise correlations among factor tensors. This architectural approach achieves a unified representation of global, local, and non-local priors for HSIs. Experimental results demonstrate that the model has strong applicability and exhibits outstanding recovery effects in various types of missing scenes such as pixels and stripes.