Wavefront sensing from multiple focal plane images is a promising technique for high-contrast imaging systems.However,the wavefront error of an optics system can be properly reconstructed only when it is very small.Th...Wavefront sensing from multiple focal plane images is a promising technique for high-contrast imaging systems.However,the wavefront error of an optics system can be properly reconstructed only when it is very small.This paper presents an iterative optimization algorithm for the direct measurement of large static wavefront errors from only one focal plane image.We first measure the intensity of the pupil image to get the pupil function of the system and acquire the aberrated image on the focal plane with a phase error that will be measured.Then we induce a dynamic phase on the tested pupil function and calculate the associated intensity of the reconstructed image on the focal plane.The algorithm will then try to minimize the intensity difference between the reconstructed image and the aberrated test image in the focal plane,where the induced phase is a variable of the optimization algorithm.The simulation shows that the wavefront of an optical system can theoretically be reconstructed with high precision,which indicates that such an iterative algorithm may be an effective way to perform wavefront sensing for high-contrast imaging systems.展开更多
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 recon...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 tile 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 ensembleis used to obtain a sparse representation in a lower dimensional space. For reconstructing the image, we propose a novel minimum total variation with block addptive sensing to balance the accuracy and eomputation time. Our experimental results show that the proposed algorithm can efficiently reconstruct colorful astronomicai images with high resolution and improve the applicability of compressed sensing.展开更多
基金funded by the National Natural Science Foundation of China (Grant Nos.11003031 and 10873024)supported by the National Astronomical Observatories’ Special Fund for AstronomyPart of the workdescribed in this paper was carried out at California State University Northridge,with support from the National Science Foundation under grant ATM-0841440
文摘Wavefront sensing from multiple focal plane images is a promising technique for high-contrast imaging systems.However,the wavefront error of an optics system can be properly reconstructed only when it is very small.This paper presents an iterative optimization algorithm for the direct measurement of large static wavefront errors from only one focal plane image.We first measure the intensity of the pupil image to get the pupil function of the system and acquire the aberrated image on the focal plane with a phase error that will be measured.Then we induce a dynamic phase on the tested pupil function and calculate the associated intensity of the reconstructed image on the focal plane.The algorithm will then try to minimize the intensity difference between the reconstructed image and the aberrated test image in the focal plane,where the induced phase is a variable of the optimization algorithm.The simulation shows that the wavefront of an optical system can theoretically be reconstructed with high precision,which indicates that such an iterative algorithm may be an effective way to perform wavefront sensing for high-contrast imaging systems.
基金Supported by the National Natural Science Foundation of China
文摘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 tile 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 ensembleis used to obtain a sparse representation in a lower dimensional space. For reconstructing the image, we propose a novel minimum total variation with block addptive sensing to balance the accuracy and eomputation time. Our experimental results show that the proposed algorithm can efficiently reconstruct colorful astronomicai images with high resolution and improve the applicability of compressed sensing.
