Multi-Object Deep-Field Digital Holographic Imaging Based on Inverse Cross-Correlation

Abstract

To address the complexity of small or unique reconstruction distances in digital holography, we propose an inverse cross-correlation-based algorithm for the digital holographic imaging of multiplanar objects with a large depth of field. In this method, a planar output mapping is closely around the objects, and it is established by calculating the image inverse cross-correlation matrix of the reconstructed image at similar reconstruction distances, whereby the object edges serve as the result guide. Combining the search for edge planes with the depth estimation operator, the depth of field of digital holography is improved, thus allowing for a digital holography that is capable of meeting the requirements of the holographic imaging of multiplanar objects. Compared with the traditional depth estimation operator method, the proposed method solves the reconstruction ambiguity problem in multiple planes with a simple optical path, and no additional optical or mechanical devices need to be added, thus greatly improving the reconstruction quality. The numerical calculation results and the experimental results with multiplanar samples validate the effectiveness of the proposed method.