Fast Prototype Based Noise Reduction

Authors:
Kajsa Tibell;Hagen Spies;Magnus Borga
Affiliations:
Sapheneia Commercial Products AB, Linkoping, Sweden 583 30;Sapheneia Commercial Products AB, Linkoping, Sweden 583 30;Department of Biomedical Engineering, Linkoping University, Linkoping, Sweden
Venue:
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Year:
2009

Citing 5
Cited 0

Approximate nearest neighbors: towards removing the curse of dimensionality

STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
A two-dimensional interpolation function for irregularly-spaced data

ACM '68 Proceedings of the 1968 23rd ACM national conference
Higher-Order Image Statistics for Unsupervised, Information-Theoretic, Adaptive, Image Filtering

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions

Communications of the ACM - 50th anniversary issue: 1958 - 2008
Universal discrete denoising: known channel

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a novel method for noise reduction in medical images based on concepts of the Non-Local Means algorithm. The main objective has been to develop a method that optimizes the processing speed to achieve practical applicability without compromising the quality of the resulting images. A database consisting of prototypes, composed of pixel neighborhoods originating from several images of similar motif, has been created. By using a dedicated data structure, here Locality Sensitive Hashing (LSH), fast access to appropriate prototypes is granted. Experimental results show that the proposed method can be used to provide noise reduction with high quality results in a fraction of the time required by the Non-local Means algorithm.