Fast Non-Local Means (NLM) Computation With Probabilistic Early Termination
- 18 December 2009
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Signal Processing Letters
- Vol. 17 (3), 277-280
- https://doi.org/10.1109/lsp.2009.2038956
Abstract
A speed up technique for the non-local means (NLM) image denoising algorithm based on probabilistic early termination (PET) is proposed. A significant amount of computation in the NLM scheme is dedicated to the distortion calculation between pixel neighborhoods. The proposed PET scheme adopts a probability model to achieve early termination. Specifically, the distortion computation can be terminated and the corresponding contributing pixel can be rejected earlier, if the expected distortion value is too high to be of significance in weighted averaging. Performance comparative with several fast NLM schemes is provided to demonstrate the effectiveness of the proposed algorithm.This publication has 8 references indexed in Scilit:
- Efficient Nonlocal Means for Denoising of Textural PatternsIEEE Transactions on Image Processing, 2008
- Fast Non-Local Algorithm for Image DenoisingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Optimal Spatial Adaptation for Patch-Based Image DenoisingIEEE Transactions on Image Processing, 2006
- Fast image and video denoising via nonlocal means of similar neighborhoodsIEEE Signal Processing Letters, 2005
- A Review of Image Denoising Algorithms, with a New OneMultiscale Modeling & Simulation, 2005
- Image Quality Assessment: From Error Visibility to Structural SimilarityIEEE Transactions on Image Processing, 2004
- Probabilistic partial-distance fast matching algorithms for motion estimationIEEE Transactions on Circuits and Systems for Video Technology, 2001
- An improvement of the minimum distortion encoding algorithm for vector quantizationIEEE Transactions on Communications, 1985