Unsupervised Super Resolution Reconstruction of Traffic Surveillance Vehicle Images

The surveillance of public transportation is of great significance to improve public safety. However, the low resolution of vehicle images becomes a bottleneck in real scenarios. Since high-low resolution vehicle images pairs are not available in traffic surveillance scenarios, this paper aims to study the problem of unsupervised super-resolution to reconstruct the high quality vehicle image. Most of the existing super-resolution algorithms adopt pre-defined down-sampling methods for paired training, however, the models trained in this pattern cannot achieve the expected results in traffic surveillance scenarios. Therefore, we propose a super-resolution method that does not require paired data, and raise a novel down-sampling network to generate low-resolution images of vehicles close to the real-world data, and then utilize the synthesized pairs for pair-wise training. Our extensive experiments on private real-world dataset Vehicle5k demonstrate the advantages of the proposed approach over baseline approaches.