We introduce a nonlocal discrete regularization framework on weighted graphs of the
arbitrary topologies for image and manifold processing. The approach considers the …

The search for efficient image denoising methods is still a valid challenge at the crossing of
functional analysis and statistics. In spite of the sophistication of the recently proposed …

Decoupling local geometric features from the spatial location of a mesh is crucial for feature-
preserving mesh denoising. This paper focuses on first order features, ie, facet normals, and …

We propose a method for accelerating a broad class of non-linear filters that includes the
bilateral, non-local means, and other related filters. These filters can all be expressed in a …

In this article, we present GCN-Denoiser, a novel feature-preserving mesh denoising
method based on graph convolutional networks (GCNs). Unlike previous learning-based …

This paper contributes two novel techniques in the context of image restoration by nonlocal
filtering. First, we introduce an efficient implementation of the nonlocal means filter based on …

Mesh denoising is a classical, yet not well-solved problem in digital geometry processing.
The challenge arises from noise removal with the minimal disturbance of surface intrinsic …

Recent advances in scanning technologies, in particular devices that extract depth through
active sensing, allow fast scanning of urban scenes. Such rapid acquisition incurs …

Mesh surface denoising is a fundamental problem in geometry processing. The main
challenge is to remove noise while preserving sharp features (such as edges and corners) …

Reliable estimation of visual saliency is helpful to guide many computer graphics tasks
including shape matching, simplification, segmentation, etc. Inspired by basic principles …