Wavelets, Curvelets and Multiresolution Analysis Techniques Applied to Implosion Symmetry Characterization of ICF Targets

TL;DR

This paper applies wavelet, curvelet, and multiresolution analysis techniques to evaluate the symmetry of imploding shells in inertial confinement fusion targets, improving image denoising and symmetry quantification methods.

Contribution

It introduces combined multiresolution techniques for denoising and analyzing ICF shell images, comparing their effectiveness and proposing a new approach for symmetry assessment.

Findings

Curvelet transform outperforms wavelets in denoising

Undecimated wavelet decompositions are more effective than decimated ones

Combined wavelet and curvelet filtering offers minimal gains over curvelets alone

Abstract

We introduce wavelets, curvelets and multiresolution analysis techniques to assess the symmetry of X ray driven imploding shells in ICF targets. After denoising X ray backlighting produced images, we determine the Shell Thickness Averaged Radius (STAR) of maximum density, r*(N, {\theta}), where N is the percentage of the shell thickness over which to average. The non-uniformities of r*(N, {\theta}) are quantified by a Legendre polynomial decomposition in angle, {\theta}. Undecimated wavelet decompositions outperform decimated ones in denoising and both are surpassed by the curvelet transform. In each case, hard thresholding based on noise modeling is used. We have also applied combined wavelet and curvelet filter techniques with variational minimization as a way to select the significant coefficients. Gains are minimal over curvelets alone in the images we have analyzed.