Transforming Sketches into Realistic Images: Leveraging Machine Learning and Image Processing for Enhanced Architectural Visualization

Abstract

This article presents a novel approach for transforming architectural sketches into realistic images through the utilization of machine learning and image processing techniques. The proposed method leverages the Stable Diffusion model, a deep learning framework specifically designed for text-to-image generation. By integrating image processing algorithms into the workflow, the model gains a better understanding of the input sketches, resulting in visually coherent and meaningful output images. The study explores the application of the Stable Diffusion model in the context of architectural design, showcasing its potential to enhance the visualization process and support designers in generating accurate and compelling representations. The efficacy of the method is evaluated through qualitative assessment, demonstrating its effectiveness in bridging the gap between initial sketches and photorealistic renderings. This research contributes to the growing body of knowledge on the integration of machine learning and image processing in architecture, providing insights and practical implications for architects, design professionals and researchers in the field.

Keywords

• architectural visualization
• sketch-to-image transformation
• machine learning
• image processing
• stable diffusion model.