Super-resolution reconstruction of remote sensing images based on SRGAN

Meng Yu, Hongjuan Wang, Chang Liu, Deping Lin
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Abstract

In the field of remote sensing images, due to the limitations of hardware equipment, image transmission, natural environment and other reasons, the resolution of the obtained remote sensing images cannot reach the desired resolution. The emergence of image super-resolution reconstruction technology can improve the resolution of remote sensing images without increasing the high cost. Image super-resolution reconstruction refers to the fact that low-resolution images can obtain high-resolution images through certain algorithmic techniques. With the rapid development of deep learning ideas, researchers have applied it to the field of image super-resolution reconstruction and achieved good results. Image super-resolution reconstruction also shifts from traditional reconstruction methods to deep learning-based methods. The emergence of the idea of Generative Adversarial Networks has further advanced the field of image super-resolution reconstruction. By using the idea of Generative Adversarial Network (GAN), researchers can obtain more realistic high-resolution images. This paper mainly uses the SRGAN model, the image dataset DIV2K for super-resolution reconstruction, and uses a dense residual structure in the generator network to obtain more image information, so that the effect of image reconstruction is more realistic. Through the experimental verification on the SIRI-WHU remote sensing test data set, the two evaluation indicators of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are compared, and the effect is improved. Better generation results can also be observed through subjective human vision.
基于SRGAN的遥感图像超分辨率重建
在遥感图像领域,由于硬件设备、图像传输、自然环境等原因的限制,所获得的遥感图像的分辨率无法达到预期的分辨率。图像超分辨率重建技术的出现可以在不增加高成本的情况下提高遥感图像的分辨率。图像超分辨率重建是指低分辨率图像通过一定的算法技术可以获得高分辨率图像。图像超分辨率重建也从传统的重建方法转向基于深度学习的方法。生成对抗网络思想的出现,进一步推动了图像超分辨率重建领域的发展。利用生成对抗网络(GAN)的思想,研究人员可以获得更真实的高分辨率图像。本文主要采用SRGAN模型,图像数据集DIV2K进行超分辨率重建,并在生成器网络中采用密集残差结构获取更多图像信息,使图像重建效果更加逼真。通过在SIRI-WHU遥感测试数据集上的实验验证,比较了峰值信噪比(PSNR)和结构相似度(SSIM)两个评价指标,提高了效果。通过人的主观视觉也可以观察到更好的生成结果。
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