Aerospace Research Center, The Ohio State University, 2300 West Case Road, Columbus, OH 43235, USA.
Sensors (Basel). 2018 Sep 13;18(9):3075. doi: 10.3390/s18093075.
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.
由于物体在成像仪有限的曝光时间内移动，导致叶片边缘模糊，因此对运动表面进行压力敏感漆 (PSP) 成像存在问题。当使用具有长寿命的高灵敏度 PSP 时，这个模糊问题尤其具有挑战性，因为指数光衰减的长荧光时间常数会导致激发光能量的突发后产生模糊的图像。一种改善这种效果的方法是使用基于荧光时间常数估计的点扩散函数 (PSF) 进行图像反卷积。为了减少计算时间，以前的 PSP 去模糊图像反卷积实现依赖于空间不变的时间常数。然而，使用假设的时间常数会导致点扩散函数中的误差，特别是在涉及强压力梯度（导致衰减时间常数的强空间梯度）时。这项工作引入了一种迭代的图像反卷积方法，其中使用了空间变化的 PSF。由于时间常数取决于局部压力值，而局部压力值只能从简化的 PSP 数据中找到，因此以迭代的方式找到逐点 PSF 值。该方案以亚像素分辨率估计具有空间变化的超分辨率模糊核，而无需对模糊图像进行滤波，然后使用经典的迭代正则化工具来恢复图像。已经使用无核正向模型生成具有已知压力表面图和不同量噪声的测试图像，以评估该方案在不同实验条件下的适用性。还使用了带有掠射氮气射流的旋转圆盘装置来产生强压力梯度，以解决实际问题。显示了包括收敛历史和正则化迭代次数的影响的结果，并与以前的 PSP 去模糊方法进行了比较。
