## 1 Introduction
In [6], the parametric modeling of heritage buildings using Terrestrial Laser Scanning (TLS) or Structure from Motion (SfM) data within a Rhino + Grasshopper-ArchiCAD workflow for HBIM projects is enhanced. Apart from that, there are not many papers on data-driven image analysis in the field. Deep learning has been a milestone for the restoration image task. In [7], coronary computed tomography angiography was restored by reducing noise using deep learning-based image restoration (DLR). Similar to this, a deep learning model is used in [8] to remove honeycomb patterns and improve resolution in another type of medical image, a fiber bundle. Another restoration image work with medical images is [9], where low-dose CT (LDCT) images are denoised by using GAN training with autoencoders and CNNs. FormResNet is a CNN-based model presented by [10] that restores images by learning the structured details and recovering the latent clean image. Other works using similar approaches but applied to X-ray Computed Tomography (CT) are [11; 12]. Finally, a model based on Transformers is applied for image restoration in [13]. In the case of architecture and cultural heritage, we find a review paper authored by [14]. In [15], GANs are used for the reconstruction of 3D models of architectural elements. Another work is [16], which presents a semi-supervised 3D model reconstruction framework with GAN models. It is applied to the reconstruction of scenes containing architectonical buildings. An approach aimed to reconstruct the faces of statutes with a particular type of GAN called Wasserstein is described in [17]. The research proposed by [18] uses CNNs to classify cultural heritage images, enhancing image management and search efficiency. The paper by [19] presents ReCRNet, a deep learning model that outperforms existing methods in detecting concrete cracks, making it highly effective for monitoring the structural health of historical buildings. Then, an automated deep learning system using Faster R-CNN to detect and classify four types of damage in outdoor stone cultural properties is presented in [20]. Also, there is a study by [21] that presents a custom YOLOv5 deep learning model for detecting and localizing four types of defects in cultural heritage structures. Finally, in [22], a CNN system with transfer learning is developed to assess the condition of building facades in the historic Lasem District (Central Java). This work builds on the methods presented in [3], with a focus on the performance using the direct method. This method, which does not rely on segmented images, reconstructs the temple from a single image of the ruins. In contrast, the segmented method described in [3] involves two steps: first, segmenting the image by architectural elements, and second, reconstructing the temple using the segmented image, which is less efficient in computational terms. Testing various hyperparameters and analyzing the results from an architectural perspective are key aspects of this study. The evaluation is based on subjective and objective methods that consider the reconstruction of the architectonical elements considering the problems produced by graphic artifacts and other issues. The rest of the paper is structured as follows. Section 2 describes the dataset used to train the models and defines the methods applied in the work. Section 3 compiles all the results related to the training workflow and both evaluations: the objective and the subjective. Finally, Section 4 obtains some conclusions and points out several future works.