# Transforming Monochromatic Images into 3D Holographic Stereograms Through Depth-Map Extraction
## 5 Conclusions  
This paper presents an innovative approach for generating 3D digital stereoscopic holograms from a single black-and-white image. Our method integrates AI-based colorization and depth-map estimation with video-editing techniques to create 3D scenes and render perspective images. This pipeline allows the generation of holographic portraits from historical images, offering a novel method for cultural preservation and personal archiving when traditional techniques are limited by the availability of input data. Our results confirm the feasibility of producing visually compelling 3D holograms from monochromatic images. The combination of AI-driven colorization and depth estimation is effective for reconstructing realistic 3D representations. Quantitative comparisons show that our approach achieved an accuracy comparable to that of state-of-the-art single-image depth-estimation algorithms, whereas subjective evaluations indicate that the generated holograms exhibited convincing depth and realism. One limitation of the proposed method is the achievable FOV of the generated holographic stereograms. While a wider FOV may seem desirable for a more immersive experience, an FOV of approximately 100\({}^{\circ}\) introduces significant distortions owing to increased parallax and perspective effects that are not accurately represented in our rendering pipeline. To maintain image quality and ensure a comfortable viewing experience, we limited the FOV to be within a range of 85-100\({}^{\circ}\). Despite these constraints, the quality of the final hologram remains highly dependent on the accuracy of the generated depth map and the FOV of the 3D scene. Future research should focus on addressing the complex challenge of generating 360\({}^{\circ}\) holographic portraits from single monocular images. Achieving this goal requires the development of new depth-estimation and 3D-reconstruction techniques that can accurately predict the complete geometry of a subject using minimal input data. We anticipate that advancements in both holographic display technology and deep learning models are essential for realizing this aim. In addition, expanding the application of our method to a broader range of historical images and exploring alternative holographic printing techniques can further enhance the scope and impact of this study. Despite its limitations, our study introduces a valuable new approach for bridging the gap between historical photography and modern holographic technology. By enabling the creation of lifelike 3D representations from minimal input data, this method opens new possibilities for memory preservation, historical visualization, and interactive engagement with the past. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15105699/s1](https://www.mdpi.com/article/10.3390/app15105699/s1)]([https://www.mdpi.com/article/10.3390/app15105699/s1](https://www.mdpi.com/article/10.3390/app15105699/s1)). Video SI: Supplementary Material. Conceptualization, O.M.N. ; methodology, O.M.N. and J.F.U.A. ; software, O.M.N. and J.C.; validation, O.M.N. and P.G. ; formal analysis, O.M.N. ; investigation, O.M.N. ; resources, P.G. and S.L. ; data curation, O.M.N. ; writing--original draft preparation, O.M.N. ; writing--review and editing, O.M.N. ; visualization, O.M.N. ; supervision, L.H. and S.L. ; project administration, L.H. and S.L. ; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Original Image** & **Proposed Methods** \\ \hline Resolution (pixels) & 500 \(\times\) 393 & 880 \(\times\) 660 \\ Brightness & 133.50 & 165.85 \\ Contrast & 79.26 & 86.24 \\ Sharpness & 58.34 & 313.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparison of image quality metrics between the original image and the proposed holographic reconstruction.