Jia Yanfei, Yu Wenshuo, Zhao Liquan
College of Electrical and Electronic Information Engineering, Beihua University, Jilin, 132013, China.
Department of Instrument Science and Electrical Engineering, Jilin University, Changchun, 130061, China.
Sci Rep. 2024 Dec 28;14(1):31426. doi: 10.1038/s41598-024-83088-x.
The scattering of tiny particles in the atmosphere causes a haze effect on remote sensing images captured by satellites and similar devices, significantly disrupting subsequent image recognition and classification. A generative adversarial network named TRPC-GAN with texture recovery and physical constraints is proposed to mitigate this impact. This network not only effectively removes haze but also better preserves the texture information of the original remote sensing image, thereby enhancing the visual quality of the dehazed image. A multi-scale module is proposed to extract feature information of remote sensing images, allowing it to capture image features from different receptive fields. Simultaneously, an attention module is designed further to guide the network's focus towards important feature information. In addition, a multi-scale adversarial network is proposed to better restore both global and local information about the original image. Introducing a physical constraint loss function to improve the loss function of the original generative adversarial network allows for better preservation of the physical characteristics of remote sensing images. Simulation experiments on synthetic and natural hazy remote sensing image datasets are conducted. The results demonstrate that the dehazing performance of the TRPC-GAN method surpasses the other four methods.
大气中微小颗粒的散射会对卫星及类似设备拍摄的遥感图像造成雾霾效应，严重干扰后续的图像识别和分类。为此，提出了一种具有纹理恢复和物理约束的生成对抗网络TRPC-GAN，以减轻这种影响。该网络不仅能有效去除雾霾，还能更好地保留原始遥感图像的纹理信息，从而提高去雾后图像的视觉质量。提出了一种多尺度模块来提取遥感图像的特征信息，使其能够从不同的感受野捕捉图像特征。同时，设计了一个注意力模块，进一步引导网络关注重要的特征信息。此外，还提出了一种多尺度对抗网络，以更好地恢复原始图像的全局和局部信息。引入物理约束损失函数来改进原始生成对抗网络的损失函数，有助于更好地保留遥感图像的物理特征。对合成和自然模糊遥感图像数据集进行了仿真实验。结果表明，TRPC-GAN方法的去雾性能优于其他四种方法。
