Bronze Mirror Texture Restoration Based on Deep Convolutional Neural Network

In this paper, a method based on Deep Convolutional Neural Network (DCNN) for bronze mirror texture restoration is proposed. The method first trains a deep convolutional neural network model by constructing a dataset containing a large number of high-quality bronze mirror texture images, so that it can automatically learn and understand the features of bronze mirror texture. Then, for the damaged bronze mirror texture image, the trained model is used to predict and fill, and the texture is intelligently repaired. In order to improve the authenticity and detail retention of the restoration effect, we also introduced an attention mechanism and GAN structure, allowing the model to more accurately capture and restore the subtle features of the texture. The experimental results show that compared with the traditional manual repair method and other machine learning-based repair algorithms, our method has significantly improved the accuracy, efficiency and visual effect of bronze mirror texture repair.