Deconvolution-Based Algorithms for Deblurring PSP Images of Rotating Surfaces

Blurring is a problem encountered when pressure-sensitive paint (PSP) is applied to rotating surfaces such as rotorcraft blades. The issue is particularly problematic near the leading and trailing edges of the blade: these are the regions where the impact of blurring is the most significant, yet they also contain the most valuable pressure information. Recent work has developed image deblurring techniques based on deconvolution of the image with a point-spread function (PSF) based on the known lifetime decay of PSP and rotation speed of the blade. This deblurring technique is effective in recovering information at the blade edges when the amount of blurring is not too high. However, the existing deblurring algorithm assumes rectilinear motion and uniform distribution of luminophore lifetime (i.e., constant pressure distribution). The objective of this work is to relax these assumptions by allowing for rotational blur and to assess the impact of strong pressure gradients. The new deblurring scheme is evaluated by experiments on a spinning disk with a known pressure field and a co-rotating, grazing nitrogen jet. The sensitivity of the deblurred results to various input parameters is evaluated, and recommendations for further algorithm development are provided.