Extending the depth-of-field of computational ghost imaging : Computational refocus via in situ point spread function estimation

Ma, M. and Liang, W. and Qin, F. and Guan, Q. and Zhong, X. and Deng, H. and Wang, Z. (2024) Extending the depth-of-field of computational ghost imaging : Computational refocus via in situ point spread function estimation. Applied Physics Letters, 124 (2): 021106. ISSN 0003-6951

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Abstract

Capturing details of objects beyond the focal plane is challenging due to the limited depth-of-field (DoF) of optical systems. Here, we report a computational refocusing ghost Imaging (CRGI) method to extend the DoF of computational ghost imaging (CGI) systems. An ultra-fast and in situ point spread function (PSF) estimation method is put forward utilizing the optical characterization of the system and compressive sensing modulation. The PSF distribution is measured with in situ compressive sensing algorithm according to reciprocity property using the same CGI system. The convolution of PSFs of various depths with modulation patterns is reshaped into measurement matrices to computationally refocus objects at different depths. From one measurement, CRGI can rebuild distinct and well-focused images of multiple objects at different depths. According to experiments, CRGI can nearly quadruple the DoF of typical CGI methods. CRGI represents a significant advancement in CGI domain by computationally surpassing the optical DoF limitations. This discovery enables recording object features beyond the focus plane using extended depth-of-field.