# Transforming Monochromatic Images into 3D Holographic Stereograms Through Depth-Map Extraction
## 4 Results and Discussion  
### Dataset  
Experiments were conducted using a dataset consisting of two monochromatic images. The images are portrait photographs of Korean activists [PERSON] and [PERSON], sourced from Wikipedia, with a resolution of 500 \(\times\) 393 pixels. These images exhibit distinct visual characteristics, including an average brightness of 133.50, a sharpness value of 43.34, and a contrast level of 79.26. The images were chosen not only for their historical significance but also for their varying levels of image quality and complexity, enabling an assessment of the robustness of our method. The original images (Figures 7a and 8a) were first preprocessed to improve their quality. This involved removing artifacts and enhancing clarity through noise reduction. Figures 7b and 8b show the resulting enhanced images. This initial step is crucial for improving the performance of the subsequent colorization and depth-estimation steps. The CNN-based colorization method proposed by [PERSON] et al. [23] was used to enhance the grayscale images. This model, pre-trained on a large-scale dataset, effectively infers plausible color information based on both global- and local-image features. Figures 7c and 8c show the resulting colored images. Visual inspection suggests that the colorization step produced realistic images consistent with the historical context of the photographs. Depth maps were generated from the colorized images using the DCNF model [24]. This model efficiently infers depth from single images and produces more accurate and consistent depth maps than methods that rely solely on geometric cues. The generated depth maps are shown in Figures 7d and 8d, where the brighter regions represent areas closer to the viewer. Using the colorized images and their corresponding depth maps, 3D scenes were reconstructed using Adobe After Effects. Depth maps were used to displace the 2D images, creating a sense of depth and volume. Following depth-map extraction, further refinement  
Figure 8: (**a**) Original, (**b**) enhanced, and (**c**) colorized images of [PERSON]; (**d**) depth map obtained from the colorized image of [PERSON]. Figure 7: (**a**) Original, (**b**) enhanced, and (**c**) colorized images of [PERSON]; (**d**) depth map obtained from the colorized image of [PERSON]. was carried out within After Effects using built tools such as Depth Map Control and Blur. In this stage, depth values were manually adjusted using slider controls, and blur parameters were fine-tuned to create smoother and more perceptually accurate depth transitions. These adjustments were part of the 3D scene composition process and were independent of the original AI model training. Figures 9a and 9b show the resulting 3D images of [PERSON] and [PERSON], respectively. Perspective images, which are fundamental for holographic stereogram synthesis, were generated from the constructed 3D scenes in Adobe After Effects. A virtual camera array was employed, with the specific configuration of the array determined by the complexity and dimensions of the 3D scene. Each virtual camera was positioned and oriented to capture a distinct perspective of the scene. A recentering camera model was used to ensure that the focal point of each camera remained fixed at the center of the scene. This technique mitigates perspective distortion and maintains consistent scaling across the viewpoints. The trajectory and baseline of the camera array were carefully chosen to achieve a balance between capturing sufficient parallax information and minimizing geometric distortions. The After Effects rendering engine generated a sequence of perspective images, each corresponding to a unique camera viewpoint. The resolution and format of the images were selected based on the requirements of the subsequent hologram-generation process. The resulting image sequence encoded parallax information, thereby forming the foundation for constructing holographic stereograms.