Circular LBP Prior-Based Enhanced GAN for Image Style Transfer

Abstract

Image style transfer (IST) has drawn broad attention recently. At present, convolutional neural network (CNN)-based methods and generative adversarial network (GAN)-based methods have been broadly utilized in IST. However, the texture of images obtained by most methods presents a lower definition, which leads to insufficient details of IST. To this end, the authors present a new IST method based on an enhanced GAN with a prior circular local binary pattern (LBP). They utilize circular LBP in a GAN generator as a texture prior to improve the detailed textures of the generated style images. Meanwhile, they integrate a dense connection residual block and an attention mechanism into the generator to further improve high-frequency feature extraction. In addition, the total variation (TV) regularizer is integrated into the loss function to smooth the training results and restrain the noise. The qualitative and quantitative experimental results demonstrate that the metric quality of the generated images can achieve better effects by the proposed strategy compared with other popular approaches.

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