Compressive incoherent digital holography for high-fidelity 3D imaging

Incoherent digital holography has attracted significant attention due to its advantages in three-dimensional (3D) imaging under low spatial coherence conditions, such as easy access to light sources and reduced speckle noise. However, interlayer crosstalk during the reconstruction process leads to a substantial reduction in reconstruction fidelity. Furthermore, existing deconvolution- and deep-learning-based reconstruction algorithms face limitations in terms of effectiveness and generalization. To address these challenges, we propose a compressive incoherent digital holography (CIDH) approach for 3D imaging. In CIDH, a point spread hologram sequence with a high signal-to-noise ratio is initially obtained using a customized computer-generated holography method for dual-channel forward data acquisition. For scene reconstruction, a compressed sensing-based two-step iterative shrinkage/thresholding algorithm is employed to achieve high-fidelity 3D scene retrieval. The combined optimization demonstrates exceptional performance in suppressing interlayer crosstalk and enhancing reconstruction fidelity. In simulations, crosstalk was effectively suppressed across 10 depth layers. In experiments, successful suppression was achieved for both a five-layer transmissive object and a two-layer reflective 3D object, resulting in significantly improved reconstruction accuracy. The proposed framework shows great potential for applications in various incoherent source-illuminated and fluorescent 3D imaging.