Panoramic Style Feature Transfer Method Based on CycleGAN and Attention Mechanism

Because of the high cost of panoramic photographs and the small amount of existing panoramic image datasets, research on the panorama‐based deep learning is restricted. In this paper, we propose a method to generate panoramic images based on a CycleGAN network. First, a selective transfer units module is added to carry out selective transmission and conversion of feature maps. Then, a convolutional block attention module is added to strengthen the image feature processing. Style conversion technology is combined to achieve spring and winter style conversion and night and day style conversion, so as to expand the dataset of panoramic images. The experimental results show that the proposed method is effective in panoramic image generation, and the use of panoramic images for style transfer is also effective in dataset expansion.