Abstract: Urban street view imagery, as crucial forms of spatial data, has a wide range of applications in mapping services, urban 3D reconstruction, and cartography. However, since the collected street view images often face challenges such as distracting target occlusion and privacy security, necessitating meticulous preprocessing. Addressing these challenges, we propose an image inpainting algorithm based on multi-scale semantic priori guided for generating more realistic and natural static street view images. Firstly, a semantic prior network is designed to learn the multi-scale semantic priors of the missing regions of the input image to enhance the contextual information. The semantic enhancement generator adaptively fuses the multi-scale semantic prior and image features and at the same time introduces a multilevel attention shifting mechanism to refine the texture information of the image. Finally, a Markov discriminator is adopted to distinguish the generated image from the real image by adversarial training, which makes the reconstructed street scene image more realistic. Experiments on the Apolloscape dataset demonstrate that the images generated by our algorithm have achieved significant improvements in semantic structural coherence and detailed texture, solving the privacy problem in street view while providing a more reliable data base for realistic city applications.

Key words: Street view image, image inpainting, realization of urban complex environment, generative adversarial network, deep learning, moving object removal