Anisotropic Mesh Adaptation for Image Representation
This work addresses image representation for image processing applications, presenting an incremental improvement over existing methods.
The paper tackles the problem of representing images using triangular meshes by introducing anisotropic mesh adaptation (AMA) methods and a GPRAMA method based on greedy-point removal, achieving better quality than the GPRFS-ED method with lower computational cost.
Triangular meshes have gained much interest in image representation and have been widely used in image processing. This paper introduces a framework of anisotropic mesh adaptation (AMA) methods to image representation and proposes a GPRAMA method that is based on AMA and greedy-point removal (GPR) scheme. Different than many other methods that triangulate sample points to form the mesh, the AMA methods start directly with a triangular mesh and then adapt the mesh based on a user-defined metric tensor to represent the image. The AMA methods have clear mathematical framework and provides flexibility for both image representation and image reconstruction. A mesh patching technique is developed for the implementation of the GPRAMA method, which leads to an improved version of the popular GPRFS-ED method. The GPRAMA method can achieve better quality than the GPRFS-ED method but with lower computational cost.