2024/06/19 by Gianlorenzo Massaro, Massaro, Gianlorenzo
Computer Science · Engineering · Physics and Astronomy · #Advanced Vision and Imaging #Digital Holography and Microscopy #FOS: Electrical engineering #FOS: Physical sciences #Image Processing Techniques and Applications #Image and Video Processing (eess.IV) #Optics (physics.optics) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2406.13501
openalex publication_date 2024/06/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03
Correlation plenoptic imaging (CPI) is emerging as a promising approach to light-field imaging (LFI), a technique enabling simultaneous measurement of light intensity distribution and propagation direction from a scene. LFI allows single-shot 3D sampling, offering fast 3D reconstruction for a wide range of applications. However, the array of micro-lenses typically used in LFI to obtain 3D information limits image resolution, which rapidly declines with enhanced volumetric reconstruction capabilities. CPI addresses this limitation by decoupling light-field information measurement using two photodetectors with spatial resolution, eliminating the need for micro-lenses. 3D information is encoded in a four-dimensional correlation function, which is decoded in post-processing to reconstruct images without the resolution loss seen in conventional LFI. This paper evaluates the tomographic performance of CPI, demonstrating that the refocusing reconstruction method provides axial sectioning capabilities comparable to conventional imaging systems. A general-purpose analytical approach based on image fidelity is proposed to quantitatively study axial and lateral resolution. This analysis fully characterizes the volumetric resolution of any CPI architecture, offering a comprehensive evaluation of its imaging performance.