Shearlet-Based Detection of Flame Fronts

TL;DR

This paper introduces a novel complex shearlet-based algorithm for detecting flame fronts in combustion imaging data, providing edge, ridge, orientation, and curvature information, and demonstrating superior performance over existing methods.

Contribution

The paper presents a new complex shearlet-based detection method that unifies edge and ridge detection with orientation and curvature estimation, improving flame front analysis.

Findings

Effective detection of flame fronts in noisy and real-world images.

Outperforms traditional edge and ridge detection techniques.

Provides additional geometric information such as orientation and curvature.

Abstract

Identifying and characterizing flame fronts is the most common task in the computer-assisted analysis of data obtained from imaging techniques such as planar laser-induced fluorescence (PLIF), laser Rayleigh scattering (LRS), or particle imaging velocimetry (PIV). We present a novel edge and ridge (line) detection algorithm based on complex-valued wavelet-like analyzing functions -- so-called complex shearlets -- displaying several traits useful for the extraction of flame fronts. In addition to providing a unified approach to the detection of edges and ridges, our method inherently yields estimates of local tangent orientations and local curvatures. To examine the applicability for high-frequency recordings of combustion processes, the algorithm is applied to mock images distorted with varying degrees of noise and real-world PLIF images of both OH and CH radicals. Furthermore, we compare the performance of the newly proposed complex shearlet-based measure to well-established edge and ridge detection techniques such as the Canny edge detector, another shearlet-based edge detector, and the phase congruency measure.