Prediction of grinding induced subsurface damage of optical materials

Dong-xi, 吕东喜 L; Hongxiang, 王洪祥 Wang; Yan-hua, 黄燕华 Huang

doi:10.3788/ope.20132103.0680

Cited by 3 publications

(1 citation statement)

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“…CS has been applied to astronomical image processing, but most of these studies have concentrated on the feasibility of CS. Liu et al (2010) used random and sparse Fourier samples to greatly reduce the amount of measurement samples, and lower the requirements of cost and complexity in telescope systems that rely on Fourier transforms for effective rapid imaging. Kolev presented a simple CS method in the orthogonal wavelet domain (Kolev 2011).…”

Section: Introductionmentioning

confidence: 99%

Fast compression and reconstruction of astronomical images based on compressed sensing

Zhou¹,

Li²,

Liu³

et al. 2014

Res. Astron. Astrophys.

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With the fast increase in the resolution of astronomical images, the question of how to process and transfer such large images has become a key issue in astronomy. We propose a new real-time compression and fast reconstruction algorithm for astronomical images based on compressive sensing techniques. We first reconstruct the original signal with fewer measurements, according to its compressibility. Then, based on the characteristics of astronomical images, we apply Daubechies orthogonal wavelets to obtain a sparse representation. A matrix representing a random Fourier ensemble is used to obtain a sparse representation in a lower dimensional space. For reconstructing the image, we propose a novel minimum total variation with block adaptive sensing to balance the accuracy and computation time. Our experimental results show that the proposed algorithm can efficiently reconstruct colorful astronomical images with high resolution and improve the applicability of compressed sensing.

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Section: Introductionmentioning

confidence: 99%