College of Aeronautics Engineering, Air Force Engineering University, Xi'an 710038, China.
Sensors (Basel). 2018 Aug 9;18(8):2606. doi: 10.3390/s18082606.
To solve the problems of color distortion and structure blurring in images acquired by sensors during bad weather, an image dehazing algorithm based on feature learning is put forward to improve the quality of sensor images. First, we extracted the multiscale structure features of the haze images by sparse coding and the various haze-related color features simultaneously. Then, the generative adversarial network (GAN) was used for sample training to explore the mapping relationship between different features and the scene transmission. Finally, the final haze-free image was obtained according to the degradation model. Experimental results show that the method has obvious advantages in its detail recovery and color retention. In addition, it effectively improves the quality of sensor images.
为了解决传感器在恶劣天气条件下获取的图像中存在的颜色失真和结构模糊问题，提出了一种基于特征学习的图像去雾算法，以提高传感器图像的质量。首先，通过稀疏编码和各种与雾相关的颜色特征同时提取雾图像的多尺度结构特征。然后，使用生成对抗网络（GAN）进行样本训练，以探索不同特征与场景传输之间的映射关系。最后，根据退化模型获得最终的无雾图像。实验结果表明，该方法在细节恢复和色彩保持方面具有明显的优势。此外，它有效地提高了传感器图像的质量。
