We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Image descattering with synthetic polarization imaging and untrained network
Summary
This study used a synthetic polarizing camera combined with an untrained neural network to remove scattering effects and improve image quality in underwater imaging. The approach addresses the challenge of light scattering that degrades resolution and contrast in underwater optical systems.
Water scattering is a significant limiting factor for underwater imaging quality. It changes the transportation direction of the original light path, causes the attenuation of light intensity, and so on. In this work, we use a synthetic polarizing camera to capture the images with different polarization states and reduce the impact of water scattering in one step with the underwater light propagation model and the Stokes vector. In addition, an untrained deep network is designed to complete the image descattering processing. Compared with the methods based on deep learning or physical model prior, it is more efficient. This technology is suitable for use in portable underwater imaging optical systems for real-time imaging and detecting particulate matter such as microplastics and microbial particles. It also broadens the application of underwater polarization imaging.