This essay, that is based on the Mie scattering Monte Carlo methodology, simulates the actual atmospheric light transmission process by constructing various parameters, including atmosphere, cloud layer, and surface albedo, and then generates the simulated polarized radiation transmission model of sky light. The sky polarization simulation software was developed using the Qt graphics library based on this model. By data entry as with atmospheric conditions, the degree of polarization and the angle of polarization are evaluated, and the meridian degree of polarization curve and the all-sky polarization diagram would then be drawn. For the purpose of polarization measurement under actual weather circumstances, it offers data assistance and verification.
The imaging equipment working in the atmosphere will not only be limited by the performance of the imaging system, but also be affected by turbulence. In the fields of astronomical observation, ground-based remote sensing and remote monitoring, there is an urgent need for corresponding methods and technologies to eliminate the impact of atmospheric turbulence and obtain clear images. With the development of computer technology, atmospheric optics theory and image processing technology, more and more researchers hope to combine deep learning technology with atmospheric turbulence theory to reduce the impact of turbulence on imaging and obtain clear and stable images. In this paper, a turbulence image restoration technique based on Generative Adversarial Networks (GAN) is proposed, which is divided into generator network and discriminator network. The generator network is used to convert blurred images affected by turbulence into clear images. The discriminator network is used to compare the converted image with the real clear image to determine whether the image is real or generated. After the whole GAN is optimized and trained, the image transformed by the generator and the real and clear image cannot be distinguished from each other. Because the training of the GAN requires a large number of corresponding samples, it is difficult to obtain the images affected and unaffected by turbulence at the same time in real life, so this paper uses the statistical characteristics of turbulence to simulate a large number of images affected by turbulence. We used the trained GAN model to simulate turbulence image restoration and got some achievements.
As an emerging technology, division-of-focal-plane (DoFP) polarization camera have raised attention due to their integrated structure. The DoFP sensor can output real time data of polarization information. In this paper, a novel visualization method for polarization is proposed for DoFP polarization camera. This approach provides three views: linear polarization image, azimuth image and four quadrant polarization image. We also use pseudo color characterization method enhances the image visualization and highlights the polarization characteristics of skylight observation targets. The experimental results show that the proposed method can output and display three polarization parameters of the incident light at each pixel: intensity, linear polarization and azimuth in real time. Imaging is intuitive we can clearly see the polarization state distribution of skylight and the polarization characteristics of the target building.
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.