Abstract
Architectural photography style transfer, a task in computer vision, employs deep learning algorithms to transform the style of architectural photograph while preserving key structure and content. Existing algorithms face challenges due to the intricate details of buildings, including diverse shapes, lines, and textures. Moreover, considerations for artistic effects in architectural photography style transfer, such as lighting, shadows, and atmosphere, require high-quality image generation algorithms. However, current algorithms often struggle to address these complexities, leading to loss or blurring of details and less realistic images. To overcome these challenges, this paper proposes a Photorealistic Attention Style Transfer Network. The proposed approach utilizes a semantic segmentation model to accurately segment the input image into foreground and background components for independent style transfer. Subsequently, the transferred images are refined by focusing on intricate building parts using the coordinate attention mechanism. Additionally, the network incorporates loss functions to capture light, shadow, and colors in stylish images, ensuring realism while maintaining aesthetic appeal. Through comparative experiments, the proposed network shows better performance in terms of image fidelity and overall aesthetics, and the SSIM and PSNR indices are also better than the current mainstream methods.