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Activity Number: 319
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Imaging
Abstract #319789
Title: Fast Translation Invariant Multiscale Image Denoising
Author(s): Meng Li* and Subhashis Ghosal
Companies: Duke University and North Carolina State University
Keywords: image denoising ; multiscale analysis ; cycle spinning ; translation invariant ; Gibbs phenomenon ; 3-dimensional image
Abstract:

Translation Invariant (TI) cycle spinning is an effective method for removing artifacts from images. However, for a method using $O(n)$ time, the exact TI cycle spinning by averaging all possible circulant shifts requires $O(n^2)$ time where $n$ is the number of pixels, and therefore is not feasible in practice. Multiscale methods based on likelihood decomposition such as penalized likelihood estimator and Bayesian methods, have become popular in image processing because of their effectiveness in denoising images. We propose a Fast Translation Invariant (FTI) algorithm and a more general $k$-Translation-Invariant ($k$-TI) algorithm, which are applicable to general $d$-dimensional images ($d > 1$) with either Gaussian or Poisson noise. The proposed FTI leads to the exact TI estimation but only requires $O(n \log_2 n )$. The proposed $k$-TI can achieve almost the same performance as the exact TI estimation, but requires even less time. We achieve this by exploiting the regularity present in the multiscale structure. The proposed FTI and $k$-TI are generic in that they are applicable on any smoothing techniques based on the multiscale structure. Matlab toolboxes are available online.


Authors who are presenting talks have a * after their name.

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