Transformer-CNN-based Multi-feature Aggregation Algorithm for Real Battlefield Image Dehazing
The development of military intelligence systems has a great influence on the fighting mode and winning mechanism of modern war. However,these systems are easily affected by haze and other bad weather conditions,resulting in blurred and degraded images,which brings challenges to the subsequent combat missions such as identification and tracking. Therefore,it is essential to restore the haze-free images on the battlefield. Since it is hard to capture the paired clean/hazy images,most existing methods adopt synthetic data for training. However,the gap between the real and synthetic hazy images will lead to the poor generalization of a model trained on synthetic data in the real world. To this end,a Transformer-CNN-based multi-feature aggregation algorithm is proposed for real battlefield image dehazing. This network adopts a semi-supervised framework to train the model with synthetic and real hazy images so that the model can better deal with the real hazy images. The algorithm applies a two branch feature aggregation architecture to aggregate the local features extracted by CNN branch and the global features extracted by the Transformer branch to further improve the dehazing ability of the model.Moreover,a hazy battlefield image dataset is constructed to simulate the real battlefield hazy conditions.The experimental results show that,compared with 8 state-of-the-art image dehazing algorithms,the proposed algorithm performs well on both synthetic data and real images.
military intelligenceimage dehazingsemi-supervised networkTransformer-CNNfeature aggregation
万方数据
维普
