Nondestructive Image De-Blurring Based on Diffraction Blurring Model

Image de-blurring is an important research branch in computer vision. Deconvolution methods, which deconvolute the blurred images with a degradation function (or point spread function) according to estimation of the blurring cause, are commonly used in the image de-blurring on macro-scale. However, these methods are difficult to deblur an image captured by a high-magnification microscopy, where the depth-of-field of the microscopy is limit and optical diffraction is obvious, because both depth variation and optical diffraction can result in blurring imaging. Due to the complicated coupling of depth and optical diffraction in micro/nano blurring imaging, the degradation function of each point may be different, and it is not reasonable to estimate it in the geometrical optics where optical diffraction is not considered. Therefore, the accuracy of these deconvolution methods is limit because their degradation functions do not include the influence of optical diffraction. In this paper, we researched the image blurring degradation process based on the theoretical relationship between the blurring degree and the depth variation, as well as optical diffraction, and then proposed an automatic method to calculate the degradation function of every pixel with a relationship between depth information and blurring degree. Finally, a non-destructive image de-blurring method was proposed and validated with different micro/nano scale samples. The experimental result proved the effectiveness and precision of our method.