High-Resolution Aerial Image Restoration with Latent Diffusion Models
Wei Li, Chengwei Li, Hongqian Jiang, Yong Wang, S. Felix Wu, Zhigang Wu · 2024
This paper aims to improve the performance of diffusion models in high-resolution unmanned aerial vehicle (UAV) aerial image restoration tasks. We propose an efficient image restoration algorithm based on diffusion models, named Latent-IRSDE, which consists of two parts: pretrained encoder-decoder networks and diffusion-based image restoration networks. Diffusion-based image restoration networks have the remarkable ability to generate high-quality aerial images with low-quality visions. Pretrained encoder-decoder networks transform high-resolution aerial images into small latent images. We leverage the frozen internal representations of pretrained encoder-decoder networks to perform an efficient high-resolution aerial images restoration. We select some data from an open-source dataset to create our IR-UAV2UAV dataset for evaluating our method. The experiments show that our proposed method can restore high-resolution aerial images and achieve fastest inference time in quantitative comparisons of image deblurring and deraining tasks on IR-UAV2UAV dataset with images sized at$1920 \times 1080\ \text{pixels}$.