AgriGAN: unpaired image dehazing via a cycle-consistent generative adversarial network for the agricultural plant phenotype

文献类型： 外文期刊

第一作者： Ding, Jin-Ting

作者： Ding, Jin-Ting;Peng, Yong-Yu;Huang, Min;Zhou, Sheng-Jun

作者机构：

关键词： Agricultural images; Generative adversarial networks; Image dehazing; Information extraction

期刊名称：SCIENTIFIC REPORTS （ 影响因子：3.8； 五年影响因子：4.3 ）

ISSN： 2045-2322

年卷期： 2024 年 14 卷 1 期

页码：

收录情况： SCI

摘要： Artificially extracted agricultural phenotype information exhibits high subjectivity and low accuracy, while the utilization of image extraction information is susceptible to interference from haze. Furthermore, the effectiveness of the agricultural image dehazing method used for extracting such information is limited due to unclear texture details and color representation in the images. To address these limitations, we propose AgriGAN (unpaired image dehazing via a cycle-consistent generative adversarial network) for enhancing the dehazing performance in agricultural plant phenotyping. The algorithm incorporates an atmospheric scattering model to improve the discriminator model and employs a whole-detail consistent discrimination approach to enhance discriminator efficiency, thereby accelerating convergence towards Nash equilibrium state within the adversarial network. Finally, by training with network adversarial loss + cycle consistent loss, clear images are obtained after dehazing process. Experimental evaluations and comparative analysis were conducted to assess this algorithm's performance, demonstrating improved accuracy in dehazing agricultural images while preserving detailed texture information and mitigating color deviation issues.

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