用改进GAN提升卫星图像去雾效果，支持多尺度特征融合。
IncepDeHazeGAN: Novel Satellite Image Dehazing
- 引入Inception模块与多层特征融合，增强多尺度信息提取
- 在多个数据集上达到当前最优去雾性能
- 结合Grad-CAM分析网络关注区域，适合遥感图像处理研究者
去雾是计算机视觉中提升雾霾或云层条件下拍摄图像视觉质量的技术。本文提出IncepDeHazeGAN，一种基于生成对抗网络（GAN）的新型单图去雾方法，融合Inception模块与多层特征融合机制。Inception模块实现多尺度特征提取，多层特征融合则通过多次融合不同卷积层的特征，提高特征利用率。采用Grad-CAM可解释性技术分析网络注意力分布，揭示模型对不同雾霾条件的适应能力。实验表明，该方法在多个公开数据集上均取得当前最优性能。
原文摘要 · Abstract (English)
Dehazing is a technique in computer vision for enhancing the visual quality of images captured in cloudy or foggy conditions. Dehazing helps to recover clear, high-quality images from haze-affected remote sensing data. In this study, we introduce IncepDeHazeGAN, a novel Generative Adversarial Network (GAN) involving Inception block and multi-layer feature fusion for the task of single-image dehazing. Utilizing the Inception block allows for multi-scale feature extraction. On the other hand, the multi-layer feature fusion design achieves efficient reuse of features as the features extracted at different convolution layers are fused several times. Grad-CAM XAI technique has been applied to our network, highlighting the regions focused on by the network for dehazing and its adaptation to different haze conditions. Experiments demonstrate that our network achieves state-of-the-art results in several datasets.
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