![]() ![]() Basically, SR methods include Single Image Super-Resolution (SISR) and Multi-Image Super-Resolution (MISR) according to the number of LR image, because, in the field of remote sensing research, image data are not abundant. Due to the existence of multiple solutions for any pixel in a LR image, SR methods are ill-posed problems. Generally, image SR reconstruction and denoising methods mean adding useful information (HR details) to LQ images and removing useless information (noise) from LQ images, respectively. The experimental results on three different remote sensing datasets shows the feasibility of our proposed method in acquiring remote sensing images. Our RRDGAN is implemented in wavelet transform (WT) domain, since different frequency parts could be handled separately in the wavelet domain. Then, total variation (TV) regularization is used to furthermore enhance the edge details, and the idea of Relativistic GAN is explored to make the whole network converge better. The generative network is implemented by fusing Residual Neural Network (ResNet) and Dense Convolutional Network (DenseNet) in order to consider denoising and SR problems at the same time. To address these problems, a method of reconstructing HQ remote sensing images based on Generative Adversarial Network (GAN) named “Restoration Generative Adversarial Network with ResNet and DenseNet” (RRDGAN) is proposed, which can acquire better quality images by incorporating denoising and SR into a unified framework. However, due to the complex structure and the large noise of remote sensing images, the quality of the remote sensing image obtained only by denoising method or SR method cannot meet the actual needs. Most existing methods usually only employ denoising or SR technology to obtain HQ images. Hence, denoising and super-resolution (SR) reconstruction technology are the most important solutions to improve the quality of remote sensing images very effectively, which can lower the cost as much as possible. Due to the influence of imaging equipment accuracy and atmospheric environment, HQ images are difficult to acquire, while low spatial quality (LQ) remote sensing images are very easy to acquire. High spatial quality (HQ) optical remote sensing images are very useful for target detection, target recognition and image classification. ![]()
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