Shearlet-Based Detection of Flame Fronts
This work addresses the need for accurate flame front characterization in combustion analysis, though it is incremental as it builds on existing shearlet techniques.
The paper tackled the problem of detecting flame fronts in combustion imaging data by developing a novel complex shearlet-based algorithm for edge and ridge detection, which outperformed established methods like the Canny edge detector in tests on noisy mock images and real-world PLIF data.
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.