ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Bidirectional mapping and negative correlation algorithm for aerial image dehazing
Received date: 2024-12-11
Revised date: 2025-01-10
Accepted date: 2025-02-21
Online published: 2025-03-19
Supported by
National Natural Science Foundation of China(62576192)
Recently， deep neural networks based aerial image dehazing techniques have achieved remarkable performance. However， most methods only learn a one-way mapping from hazy images to clear images， which fail to fully exploit the latent supervision information of haze generation， leading to dehazing performance degradation. Moreover， existing methods that incorporate the atmospheric scattering model in the feature space overlook the exploration of the negative correlation between the transmission map and atmospheric light， which is crucial for leaning more representative features. To address these issues， we propose a bidirectional mapping physics-aware negative correlation algorithm for aerial image dehazing. Specifically， the proposed bidirectional mapping multi-stage framework synchronously models haze removal and generation processes in a parameter-sharing network， effectively exploring the haze formation mechanism to enhance dehazing performance. Furthermore， an atmospheric light-guided physics-aware negative correlation module is designed， which optimizes transmission map estimation by leveraging a negative correlation guidance mechanism utilizing atmospheric light information. Experimental results on public datasets demonstrate that the proposed dehazing algorithm outperforms seven state-of-the art haze removal methods in both objective evaluation and visual quality.
Zhaoru YAO , Hang SUN , Dong REN , Li LIU , Jun WAN . Bidirectional mapping and negative correlation algorithm for aerial image dehazing[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(23) : 631652 -631652 . DOI: 10.7527/S1000-6893.2025.31652
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