Existing methods for remote sensing image denoising typically suffer from a common drawback of fuzzy edge information. In this paper, we proposed a Generative Adversarial Network(GAN) based on the residual learning and perceptual loss for image denoising. The proposed GAN is designed with the two parts: The generator network takes the high-frequency layer of noisy image as the input and outputs a clean image after training. In order to eliminate noise better while retaining more edges and details, three residual blocks are embedded in the generator and a perceptual loss function is added to learn the perceptual differences between the denoised images and the ground truth images. The discriminator network based on 70×70 PatchGAN can discern between the denoised image and the clean image through a confidence value. The experiments show that our proposed network achieves superior performances and preserve majority the edge contours and fine details from low-quality observations.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.