Open AccessIncoherent coded aperture correlation holographic imaging with fast adaptive and noise-suppressed reconstruction
Author(s) -
Yuhong Wan,
Chao Liu,
Teng Ma,
Yi Qin,
Sheng Lv
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.418918
Subject(s) - coded aperture , optics , holography , computer science , iterative reconstruction , computer vision , image quality , artificial intelligence , aperture (computer memory) , filter (signal processing) , cross correlation , noise (video) , digital holography , phase (matter) , physics , mathematics , image (mathematics) , detector , acoustics , mathematical analysis , quantum mechanics
Fast and noise-suppressed incoherent coded aperture correlation holographic imaging is proposed, which is utilized by employing an annular sparse coded phase mask together with adaptive phase-filter cross-correlation reconstruction method. Thus the proposed technique here is coined as adaptive interferenceless coded aperture correlation holography (AI-COACH). In AI-COACH, an annular sparse coded phase mask is first designed and generated by the Gerchberg-Saxton algorithm for suppressing background noise during reconstruction. In order to demonstrate the three-dimensional and sectional imaging capabilities of the AI-COACH system, the imaging experiments of 3D objects are designed and implemented by dual-channel optical configuration. One resolution target is placed in the focal plane of the system as input plane and ensured Fourier transform configuration, which is employed as reference imaging plane, and moved the other resolution target to simulate different planes of a three-dimensional object. One point spread hologram (PSH) and multiple object-holograms without phase-shift at different axial positions are captured by single-exposure sequentially with the annular sparse CPMs. A complex-reconstruction method is developed to obtain adaptively high-quality reconstructed images by employing the cross-correlation of PSH and OH with optimized phase filter. The imaging performance of AI-COACH is investigated by imaging various type of objects. The research results show that AI-COACH is adaptive to different experimental conditions in the sense of autonomously finding optimal parameters during reconstruction procedure and possesses the advantages of fast and adaptive imaging with high-quality reconstructions.