Enhancing iterative hologram generation by mutual accounting quantitative metrics

Abstract Computer-generated holography is of significant interest for applications such as holographic displays, optical tweezers, and laser beam shaping. In this study, we propose a method to improve iterative hologram generation by integrating multiple quantitative image quality metrics—including mean squared error, structural similarity index, normalized standard deviation (NSTD), correlation coefficient, and diffraction efficiency—with weighted optimization. To demonstrate the method’s effectiveness, we enhanced an iterative amplitude binary inline Fresnel hologram generation technique. Our results show that assigning a large weight to NSTD yields the best reconstruction quality: NSTD is reduced by 1.8 times (to 0.09 for binary images and 0.05 for grayscale images). Optical reconstructions using a digital micromirror device confirm these findings. This work establishes a framework for advancing CGH in high-quality 2D/3D visualization and optical encryption.