# Transforming Monochromatic Images into 3D Holographic Stereograms Through Depth-Map Extraction
## 3 Proposed Method  
### Generation of the 3D Model from a Single Image Based on Depth Maps  
This section describes the method proposed for generating 3D digital stereoscopic holograms from a single black-and-white photograph. Our approach overcomes the limitations of existing methods by leveraging readily available deep learning techniques to create and manipulate depth information from a single monochromatic input image. The process involves the following key stages: (1) image preprocessing and restoration, (2) colorization using a convolutional neural network (CNN) architecture proposed by [PERSON] et al. [23], (3) depth-map estimation using a deep convolutional neural field (DCNF) model proposed by [PERSON] et al. [24], followed by refinement using Adobe After Effect, and (4) perspective image synthesis. Figure 1 presents an overview of the proposed pipeline. Our pipeline adopts a serial architecture in which colorization precedes depth estimation. The black-and-white input is first colorized, and a depth map is then generated from the colorized result. This approach is motivated by the observation that, compared to monochromatic images, RGB images have distinct color contrasts that enhance the accuracy of object edge detection, which is crucial for extracting high-quality depth maps. As illustrated in Figure 2, the process begins with noise reduction in the grayscale input image, using a pre-trained deep learning model from the Hugging Face Model Hub. This noise reduction step enhances the quality of the input for subsequent colorization and depth-map extraction. For automatic image colorization, a joint end-to-end learning approach is used to integrate global image priors with local image features. The architecture comprises a low-level feature network, a mid-level feature network, a global feature network, and a colorization network arranged in a directed acyclic graph. This design allows the network to process images of arbitrary resolution and adapt colors based on the overall image context  
Figure 1: Overview of the proposed method. Figure 2: Detailed workflow for generating depth-map and color information from monochromatic Images. learned from large-scale scene classification datasets. The network outputs chrominance channels, which are then combined with the luminance channels from the original grayscale image to produce colorized results. We chose the CNN-based colorization model proposed by [PERSON] et al. [23] because its architecture, which incorporates both global scene priors and local image features, is well suited for colorizing legacy black-and-white photographs. Global priors help infer plausible colors based on the overall scene context, whereas local features enable the detailed colorization of individual objects. This provides an advantage over simpler colorization methods that rely solely on local pixel relationships and often produce inconsistent or unrealistic colorizations. We employed a DCNF model for depth-map estimation [24]. This model formulates depth estimation as a continuous conditional random field (CRF) learning problem by combining the strengths of deep CNNs and continuous CRFs in a unified CNN framework. The DCNF model learns the unary and pairwise potentials of a continuous CRF, facilitating depth estimation in general scenes without geometric prior or extra information. To learn the unary potential, it uses a CNN with five convolutional and four fully connected layers to regress the depth values from the image patches centered around the superpixel centroids. To learn the pairwise potential, it uses a fully connected layer to model the similarities between neighboring superpixel pairs. The CRF loss layer minimizes the negative log-likelihood using the unary and pairwise outputs. A DCNF model was selected because it offers a robust and accurate approach for inferring depth from a single image, providing more accurate and consistent depth maps than methods that rely solely on CNNs or traditional geometric cues.