Shen Helin, Zhong Tie, Jia Yanfei, Wu Chunming
Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology (Ministry of Education), Department of Communication Engineering, College of Electric Engineering, Northeast Electric Power University, Jilin, 132012, China.
College of Electric Power Engineering, Beihua Univesity, Jilin, 132012, China.
Sci Rep. 2024 May 29;14(1):12382. doi: 10.1038/s41598-024-63259-6.
Remote sensing is gradually playing an important role in the detection of ground information. However, the quality of remote-sensing images has always suffered from unexpected natural conditions, such as intense haze phenomenon. Recently, convolutional neural networks (CNNs) have been applied to deal with dehazing problems, and some important findings have been obtained. Unfortunately, the performance of these classical CNN-based methods still needs further enhancement owing to their limited feature extraction capability. As a critical branch of CNNs, the generative adversarial network (GAN), composed of a generator and discriminator, has become a hot research topic and is considered a feasible approach to solving the dehazing problems. In this study, a novel dehazed generative adversarial network (GAN) is proposed to reconstruct the clean images from the hazy ones. For the generator network of the proposed GAN, the color and luminance feature extraction module and the high-frequency feature extraction module aim to extract multi-scale features and color space characteristics, which help the network to acquire texture, color, and luminance information. Meanwhile, a color loss function based on hue saturation value (HSV) is also proposed to enhance the performance in color recovery. For the discriminator network, a parallel structure is designed to enhance the extraction of texture and background information. Synthetic and real hazy images are used to check the performance of the proposed method. The experimental results demonstrate that the performance can significantly improve the image quality with a significant increment in peak-signal-to-noise ratio (PSNR). Compared with other popular methods, the dehazing results of the proposed method closely resemble haze-free images.
遥感技术在地面信息检测中逐渐发挥着重要作用。然而，遥感图像的质量一直受到意外自然条件的影响，如强烈的雾霾现象。近年来，卷积神经网络（CNN）已被应用于处理去雾问题，并取得了一些重要成果。不幸的是，由于这些基于经典CNN的方法特征提取能力有限，其性能仍需进一步提高。作为CNN的一个关键分支，由生成器和判别器组成的生成对抗网络（GAN）已成为一个热门研究课题，并被认为是解决去雾问题的一种可行方法。在本研究中，提出了一种新型的去雾生成对抗网络（GAN），用于从模糊图像中重建清晰图像。对于所提出的GAN的生成器网络，颜色和亮度特征提取模块以及高频特征提取模块旨在提取多尺度特征和颜色空间特征，这有助于网络获取纹理、颜色和亮度信息。同时，还提出了一种基于色调饱和度值（HSV）的颜色损失函数，以提高颜色恢复性能。对于判别器网络，设计了一种并行结构以增强纹理和背景信息的提取。使用合成和真实的模糊图像来检验所提方法的性能。实验结果表明，该方法的性能可以显著提高图像质量，峰值信噪比（PSNR）有显著提高。与其他流行方法相比，所提方法的去雾结果与无雾图像非常相似。
