Imaging of pressure-sensitive paint (PSP) for pressure measurement on moving surfaces is problematic due to the movement of the object within the finite exposure time of the imager, resulting in the blurring of the blade edges. The blurring problem is particularly challenging when high-sensitivity PSP with a long lifetime is used, where the long luminescence time constant of exponential light decay following a burst of excitation light energy results in blurred images. One method to ameliorate this effect is image deconvolution using a point spread function (PSF) based on an estimation of the luminescent time constant. Prior implementations of image deconvolution for PSP deblurring have relied upon a spatially invariant time constant in order to reduce computational time. However, the use of an assumed value of time constant leads to errors in the point spread function, particularly when strong pressure gradients (which cause strong spatial gradients in the decay time constant) are involved. This work introduces an iterative method of image deconvolution, where a spatially variant PSF is used. The point-by-point PSF values are found in an iterative manner, since the time constant depends on the local pressure value, which can only be found from the reduced PSP data. The scheme estimates a super-resolved spatially varying blur kernel with sub-pixel resolution without filtering the blurred image, and then restores the image using classical iterative regularization tools. A kernel-free forward model has been used to generate test images with known pressure surface maps and a varying amount of noise to evaluate the applicability of this scheme in different experimental conditions. A spinning disk setup with a grazing nitrogen jet for producing strong pressure gradients has also been used to evaluate the scheme on a real-world problem. Results including the convergence history and the effect of a regularization-iteration count are shown, along with a comparison with the previous PSP deblurring method.
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