Nonlocal self-similarity (NSS) is an important prior that has been
successfully applied in multi-dimensional data processing tasks, e.g., image
and video recovery. However, existing NSS-based methods are solely suitable for
meshgrid data such as images and videos, but are not suitable for emerging
off-meshgrid data, e.g., point cloud and climate data. In this work, we revisit
the NSS from the continuous representation perspective and propose a novel
Continuous Representation-based NonLocal method (termed as CRNL), which has two
innovative features as compared with classical nonlocal methods. First, based
on the continuous representation, our CRNL unifies the measure of
self-similarity for on-meshgrid and off-meshgrid data and thus is naturally
suitable for both of them. Second, the nonlocal continuous groups can be more
compactly and efficiently represented by the coupled low-rank function
factorization, which simultaneously exploits the similarity within each group
and across different groups, while classical nonlocal methods neglect the
similarity across groups. This elaborately designed coupled mechanism allows
our method to enjoy favorable performance over conventional NSS methods in
terms of both effectiveness and efficiency. Extensive multi-dimensional data
processing experiments on-meshgrid (e.g., image inpainting and image denoising)
and off-meshgrid (e.g., climate data prediction and point cloud recovery)
validate the versatility, effectiveness, and efficiency of our CRNL as compared
with state-of-the-art methods.

基于连续表示的非局部方法 (CRNL) 统一了网格和非网格数据的自相似度测量，通过耦合低秩函数分解的方法实现更紧凑和高效的非局部连续群的表示，从而在多维数据处理任务中展现出比传统非局部方法更高的效力和效率。