Mr. Jieping Xu Profile

Jieping Xu

at National Univ of Defense Technology

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Publications (3)

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Proceedings Article | 24 October 2017 Paper

High-resolution wavefront reconstruction using the frozen flow hypothesis

Xuewen Liu, Yonghui Liang, Jin Liu, Jieping Xu

Proceedings Volume 10463, 104631G (2017) https://doi.org/10.1117/12.2285379

KEYWORDS: Wavefront reconstruction, Adaptive optics, Super resolution

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This paper describes an approach to reconstructing wavefronts on finer grid using the frozen flow hypothesis (FFH), which exploits spatial and temporal correlations between consecutive wavefront sensor (WFS) frames. Under the assumption of FFH, slope data from WFS can be connected to a finer, composite slope grid using translation and down sampling, and elements in transformation matrices are determined by wind information. Frames of slopes are then combined and slopes on finer grid are reconstructed by solving a sparse, large-scale, ill-posed least squares problem. By using reconstructed finer slope data and adopting Fried geometry of WFS, high-resolution wavefronts are then reconstructed. The results show that this method is robust even with detector noise and wind information inaccuracy, and under bad seeing conditions, high-frequency information in wavefronts can be recovered more accurately compared with when correlations in WFS frames are ignored.

Proceedings Article | 24 October 2017 Paper

Bayesian multi-frame super-resolution of differently exposed images

Jieping Xu, Yonghui Liang, Jin Liu, Zongfu Huang, Xuewen Liu

Proceedings Volume 10462, 104620B (2017) https://doi.org/10.1117/12.2281556

KEYWORDS: Super resolution, Image registration

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This paper presents a technique that performs multi-frame super-resolution of differently exposed images. The method first employs a coarse-to-fine image registration method to align image in both spatial and range domain. Then an image fusion method based on the maximum a posterior (MAP) is used to reconstruct a high-resolution image. The MAP cost function includes a data fidelity term and a regularized term. The data fidelity term is in the L2 norm, and the regularized term employs Huber-Markov prior which can reduce the noise and artifacts while reserving image edges. In order to reduce the influence of registration errors, the high-resolution image estimate and registration parameters are refined alternatively by minimizing the cost function. Experiments with synthetic and real images show that the photometric registration reduce the grid-like artifacts in the reconstructed high-resolution image, and the proposed multi-frame super resolution method has a better performance than the interpolation-based method with lower RMSE and less artifacts.

Proceedings Article | 24 October 2017 Paper

Coarse-to-fine geometric and photometric image registration

Jieping Xu, Jin Liu, Zongfu Huang, Yonghui Liang

Proceedings Volume 10462, 1046202 (2017) https://doi.org/10.1117/12.2281123

KEYWORDS: Image registration

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This paper presents a technique that performs coarse-to-fine image registration both in spatial and range domain. The goal of image registration is to estimate geometric and photometric parameters via minimization of an objective function in the least square sense. In order to reduce the probability of falling into a local optimal solution, the algorithm employs a coarse-to-fine strategy. In the coarse step, an illumination offset and contrast invariant feature detector which is named SURF is used to estimate affine motion parameters between the reference image and the target image, and then the intensity of corresponding pixels is used to directly estimate contrast and bias parameters based on RANSAC. In the fine step, the estimated parameters obtained in the coarse step are used as a good initial estimation, and photometric and affine motion parameters are refined alternatively via minimizing the objective function. Experiments on simulated and real images show that the proposed image registration method is superior to the feature-based method used in the coarse step and the groupwise image registration algorithm proposed by Bartoli.

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