AGD-GAN: Adaptive Gradient-Guided and Depth-supervised generative adversarial networks for ancient mural sketch extraction

Abstract

To address the overlooked issues of multi-scale detail feature extraction and disease noise suppression in mural sketch extraction, we proposed a novel generative adversarial network with Adaptive Gradient-guided and Depth-supervised (AGD-GAN), which can generate high-quality mural sketches in an unsupervised manner. AGD-GAN first enriches mural feature details at various scales by introducing a cross-channel residual attention module, significantly improving the detail extraction effects. The Adaptive Gradient-guided strategy is based on the gradient attention maps, which can adaptively adjust the weights between gradient information and detail feature preservation according to the degree of damage in the mural, further balancing the preservation of mural detail features and the suppression of disease noise. Finally, the Depth-supervised reinforces constraints on the position and shape of the sketches by introducing a depth-predicted loss function, thereby reducing background noise interference and controlling the shape of the generated sketches. We compared with eight state-of-the-art algorithms quantitatively and qualitatively, experimental results demonstrate the promising capability of the proposed AGD-GAN to extract clear, coherent, and comprehensive mural sketches. We release the source code at https://github.com/Alice77bai/AGD-GAN.