Ms. Jin-li Yao Profile

Jin-li Yao

at Ningbo Dahongying Univ

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Proceedings Article | 20 August 2013 Paper

Blind restoration method of three-dimensional microscope image based on RL algorithm

Jin-li Yao, Si Tian, Xiang-rong Wang, Jing-li Wang

Proceedings Volume 8913, 89130Z (2013) https://doi.org/10.1117/12.2034965

KEYWORDS: Point spread functions, Image restoration, Imaging systems, 3D image processing, Microscopes, Signal to noise ratio, Microscopy, Image processing, Detection and tracking algorithms, Algorithms

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Thin specimens of biological tissue appear three dimensional transparent under a microscope. The optic slice images can be captured by moving the focal planes at the different locations of the specimen. The captured image has low resolution due to the influence of the out-of-focus information comes from the planes adjacent to the local plane. Using traditional methods can remove the blur in the images at a certain degree, but it needs to know the point spread function (PSF) of the imaging system accurately. The accuracy degree of PSF influences the restoration result greatly. In fact, it is difficult to obtain the accurate PSF of the imaging system. In order to restore the original appearance of the specimen under the conditions of the imaging system parameters are unknown or there is noise and spherical aberration in the system, a blind restoration methods of three-dimensional microscope based on the R-L algorithm is proposed in this paper. On the basis of the exhaustive study of the two-dimension R-L algorithm, according to the theory of the microscopy imaging and the wavelet transform denoising pretreatment, we expand the R-L algorithm to three-dimension space. It is a nonlinear restoration method with the maximum entropy constraint. The method doesn’t need to know the PSF of the microscopy imaging system precisely to recover the blur image. The image and PSF converge to the optimum solutions by many alterative iterations and corrections. The matlab simulation and experiments results show that the expansion algorithm is better in visual indicators, peak signal to noise ratio and improved signal to noise ratio when compared with the PML algorithm, and the proposed algorithm can suppress noise, restore more details of target, increase image resolution.

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