Visual prompt-based learning for aerial image dehazing

Jiayi Lin

doi:10.1117/12.3041876

11 September 2024 Visual prompt-based learning for aerial image dehazing

Jiayi Lin

Author Affiliations +

Proceedings Volume 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024); 1325304 (2024) https://doi.org/10.1117/12.3041876
Event: Fourth International Conference on Signal Image Processing and Communication, 2024, Xi'an, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiayi Lin "Visual prompt-based learning for aerial image dehazing", Proc. SPIE 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024), 1325304 (11 September 2024); https://doi.org/10.1117/12.3041876

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