Adaptive Nonlocal Filtering: A Fast Alternative to Anisotropic Diffusion for Image Enhancement
IEEE Transactions on Pattern Analysis and Machine Intelligence
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SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
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SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
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In most cases nonlinear diffusion filtering is implemented by means of explicit finite difference schemes. These algorithms are not very efficient, since they are only stable for small time steps. We address this problem by presenting unconditionally stable semi-implicit schemes which are based on an additive operator splitting (AOS). They are very efficient since they can be implemented by recursive filtering, and their separability allows a straightforward implementation in any dimension. We analyze their behavior on a parallel computer and demonstrate that parallel AOS schemes on a modern shared-memory multiprocessor system with 8 processors allow a speed-up of two orders of magnitude in comparison to the widely-used explicit scheme on a single processor.