Speckle noise can reduce the image quality of synthetic aperture radar (SAR) and make interpretation more difficult. Existing SAR image despeckling convolutional neural networks require quantities of noisy–clean image pairs. However, obtaining clean SAR images is very difficult. Because continuous convolution and pooling operations result in losing many informational details while extracting the deep features of the SAR image, the quality of recovered clean images becomes worse. Therefore, we propose a despeckling network called multiscale dilated residual U-Net (MDRU-Net). The MDRU-Net can be trained directly using noisy–noisy image pairs without clean data. To protect more SAR image details, we design five multiscale dilated convolution modules that extract and fuse multiscale features. Considering that the deep and shallow features are very distinct in fusion, we design different dilation residual skip connections, which make features at the same level have the same convolution operations. Afterward, we present an effective L_hybrid loss function that can effectively improve the network stability and suppress artifacts in the predicted clean SAR image. Compared with the state-of-the-art despeckling algorithms, the proposed MDRU-Net achieves a significant improvement in several key metrics.
As a result of people taking more and more pictures in their lives, image assessment technology, which can automatically help people choose high quality pictures quickly, has become particularly important. Most algorithms use peak signal-to-noise ratio (PSNR) to assess image quality. However, images with high scores on PSNR are not as beautiful as individuals think. Image aesthetic assessment technology can come closer to human aesthetic standards. We report on a method named saliency symbiosis network for image aesthetic assessment. This is significant because we improved the conventional convolutional neural networks (CNN) method, which gets very close to the human visual mechanism after adding saliency features in CNN. Owing to considering limitations of CNN input size, we also proposed a pooling strategy to improve the ability of the model to accept arbitrary input sizes. Afterward, we propose an effective mean Huber loss function, which becomes less sensitive to outliers and can quickly train the model to being optimal. The experiment results proved that the proposed method, by using very small training data, performed the highest accuracy in image aesthetic assessment and classification.
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.