Application of image enhancement and mixture of Gaussian approach in combustion research

Abstract

Chemiluminescence is one of the most commonly used optical diagnostic techniques in combustion research where a line-of-sight projected information is generated from spatial fields. The exactness and uniqueness of reconstruction along with ease of implementation gives Abel inversion an edge over the other existing single-view reconstruction techniques for efficient estimation of spatial field from line-of-sight projections. Though there exist many such algorithms, the primary focus of these has been to ensure tractable inversion through a systematic regularization by imposing a smoothness constraint on discrete data points. But these techniques do not have the provision to process the input image prior to deconvolution in order to prevent accumulation of noise infiltrated during data acquisition. Another major limitation of these algorithms is to adopt the changes in characteristics of the input data points while maintaining optimal storage and time complexity. To address these issues, we have proposed a new image processing technique using standard Abel inversion for the application in combustion research. It provides a suitable model to ensure regularized inversion by imposing a smoothness constraint on acquired raw data. The new algorithm has been implemented to yield the physically significant chemiluminescence emission from hydroxyl radicals in flames from line-of-sight integrated images. The effectiveness of this algorithm is highlighted using exemplary OH chemiluminescence images captured from a standard swirl stabilized research burner.