20 research outputs found
Reflection Removal Using Recurrent Polarization-to-Polarization Network
This paper addresses reflection removal, which is the task of separating
reflection components from a captured image and deriving the image with only
transmission components. Considering that the existence of the reflection
changes the polarization state of a scene, some existing methods have exploited
polarized images for reflection removal. While these methods apply polarized
images as the inputs, they predict the reflection and the transmission directly
as non-polarized intensity images. In contrast, we propose a
polarization-to-polarization approach that applies polarized images as the
inputs and predicts "polarized" reflection and transmission images using two
sequential networks to facilitate the separation task by utilizing the
interrelated polarization information between the reflection and the
transmission. We further adopt a recurrent framework, where the predicted
reflection and transmission images are used to iteratively refine each other.
Experimental results on a public dataset demonstrate that our method
outperforms other state-of-the-art methods
Crowdsourced-based Deep Convolutional Networks for Urban Flood Depth Mapping
Successful flood recovery and evacuation require access to reliable flood
depth information. Most existing flood mapping tools do not provide real-time
flood maps of inundated streets in and around residential areas. In this paper,
a deep convolutional network is used to determine flood depth with high spatial
resolution by analyzing crowdsourced images of submerged traffic signs. Testing
the model on photos from a recent flood in the U.S. and Canada yields a mean
absolute error of 6.978 in., which is on par with previous studies, thus
demonstrating the applicability of this approach to low-cost, accurate, and
real-time flood risk mapping.Comment: 2022 European Conference on Computing in Constructio