KAUST Grant NumberKUK-C1-013-04
Permanent link to this recordhttp://hdl.handle.net/10754/597437
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AbstractWe demonstrate a method for removing noise from images or other data on curved surfaces. Our approach relies on in-surface diffusion: we formulate both the Gaussian diffusion and Perona-Malik edge-preserving diffusion equations in a surface-intrinsic way. Using the Closest Point Method, a recent technique for solving partial differential equations (PDEs) on general surfaces, we obtain a very simple algorithm where we merely alternate a time step of the usual Gaussian diffusion (and similarly Perona-Malik) in a small 3D volume containing the surface with an interpolation step. The method uses a closest point function to represent the underlying surface and can treat very general surfaces. Experimental results include image filtering on smooth surfaces, open surfaces, and general triangulated surfaces. © 2013 IEEE.
CitationBiddle H, von Glehn I, Macdonald CB, Marz T (2013) A volume-based method for denoising on curved surfaces. 2013 IEEE International Conference on Image Processing. Available: http://dx.doi.org/10.1109/ICIP.2013.6738109.
SponsorsThe work of all authors was partially supported by Award No KUK-C1-013-04 made by King Abdullah University of Science and Technology(KAUST).