Visual prompt-based learning for aerial image dehazing
Jiayi Lin · 2024
In response to the limited generalization capability of deep learning-based methods for aerial image dehazing across various levels of haze degradation, based on visual prompt learning methods for image dehazing is proposed. The algorithm mainly utilizes a multi-scale encoder-decoder architecture based on U-Net, introducing Prompt learning in the decoding stage to further enhance the generalization performance of image dehazing. Firstly, soft cue techniques are used to generate learnable parameters, which adaptively adjust the weight values according to the features, thereby encoding the discrimination information for various types of haze degradation. Secondly, by interacting the cue components with the main feature extraction network, the algorithm dynamically guides the network using different levels of degradation information to direct the image reconstruction process. Experimental results show that the proposed algorithm achieves higher dehazing performance and better visual restoration quality under three levels of haze on the benchmark dataset SateHaze1k.