Physics-aware cross-domain fusion aids learning-driven computer-generated holography

Abstract

The rapid advancement of computer-generated holography has bridged deep learning with traditional optical principles in recent years. However, a critical challenge in this evolution is the efficient and accurate conversion from the amplitude to phase domain for high-quality phase-only hologram (POH) generation. Existing computational models often struggle to address the inherent complexities of optical phenomena, compromising the conversion process. In this study, we present the cross-domain fusion network (CDFN), an architecture designed to tackle the complexities involved in POH generation. The CDFN employs a multi-stage (MS) mechanism to progressively learn the translation from amplitude to phase domain, complemented by the deep supervision (DS) strategy of middle features to enhance task-relevant feature learning from the initial stages. Additionally, we propose an infinite phase mapper (IPM), a phase-mapping function that circumvents the limitations of conventional activation functions and encapsulates the physical essence of holography. Through simulations, our proposed method successfully reconstructs high-quality 2K color images from the DIV2K dataset, achieving an average PSNR of 31.68 dB and SSIM of 0.944. Furthermore, we realize high-quality color image reconstruction in optical experiments. The experimental results highlight the computational intelligence and optical fidelity achieved by our proposed physics-aware cross-domain fusion.