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Proceedings Paper

Hyper-Laplacian priors combined with rotating pupils for image restoration in sparse aperture systems
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Paper Abstract

A hyper-Laplacian can model the heavy-tailed distribution of gradients in natural scenes well, which have proven effective priors for deconvolution and denoising. However, because of missing point spread function (PSF) information in the two-dimensional spatial domain of optical sparse aperture (OSA) systems, a hyper-Laplacian prior of single exposure cannot recover the missing information of images. The main focus of this paper is on combining hyper-Laplacian priors with a pupil and its rotated pupils to compensate PSF information and improve the image quality in OSA systems. A scheme of rotating the pupil that has double apertures is analyzed. The cost function relative to multiple degraded images and PSFs obtained by rotating the pupil is established. The alternating minimization algorithm consisting of two phases is implemented to acquire restored images. In one phase, the non-convex part of the problem is solved. In the other phase, the fast Fourier transforms (FFTs) are used to solve a quadratic equation in the frequency domain. Using the peak signal-to noise ratio (PSNR), a quantitative analysis is provided. Simulation results show that hyper-Laplacian priors combined with rotating pupils can restore images better than a hyper-Laplacian prior of single exposure in an OSA system. Taking spoke-square image as the test image, the PSNR is 28.34 dB with two rotations and 23.52 dB without rotation. Moreover, the numbers of rotating the pupil that lead to different changes of the image quality are demonstrated.

Paper Details

Date Published: 6 September 2019
PDF: 7 pages
Proc. SPIE 11135, Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing 2019, 111350H (6 September 2019); doi: 10.1117/12.2527381
Show Author Affiliations
Luting Zhang, Beijing Institute of Technology (China)
Yuejin Zhao, Beijing Institute of Technology (China)
Liquan Dong, Beijing Institute of Technology (China)
Ming Liu, Beijing Institute of Technology (China)
Haoyuan Du, Beijing Institute of Technology (China)

Published in SPIE Proceedings Vol. 11135:
Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing 2019
Jean J. Dolne; Mark F. Spencer; Markus E. Testorf, Editor(s)

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