Oil Painting Style Transfer Algorithm and Practice Based on Generative Artificial Intelligence

To address the challenges of unstable generated image quality and insufficient detail reproduction in oil painting style transfer methods, this paper introduces an algorithm combining a generative adversarial network (GAN) and a convolutional neural network (CNN).This algorithm optimizes the extraction of oil painting style features and image detail control to realize high-quality oil painting style transfer.This paper investigates the extraction of image content features using a CNN and the capture of image style features using the Gram matrix.The optimized loss function for the generated image incorporates deep features at the texture level, enabling style transfer.The experiments utilize Python 3.7+ and the PyTorch/TensorFlow deep learning framework for image processing.Data shows that the texture detail score for both Impressionist-style and landscape-style images is 4.6, with minimal differences between the generated and target-style images, demonstrating excellent style transfer performance.Detail control metrics such as texture reconstruction quality, color saturation, and gradient smoothness are significantly improved during the transfer of the target oil painting style.