Learning to Remove Wrinkled Transparent Film with Polarized Prior
Jiaqi Tang Ruizheng Wu Xiaogang Xu Sixing Hu Ying-Cong Chen
In this paper, we study a new problem, Film Removal (FR), which attempts to
remove the interference of wrinkled transparent films and reconstruct the
original information under films for industrial recognition systems. We first
physically model the imaging of industrial materials covered by the film.
Considering the specular highlight from the film can be effectively recorded by
the polarized camera, we build a practical dataset with polarization
information containing paired data with and without transparent film. We aim to
remove interference from the film (specular highlights and other degradations)
with an end-to-end framework. To locate the specular highlight, we use an angle
estimation network to optimize the polarization angle with the minimized
specular highlight. The image with minimized specular highlight is set as a
prior for supporting the reconstruction network. Based on the prior and the
polarized images, the reconstruction network can decouple all degradations from
the film. Extensive experiments show that our framework achieves SOTA
performance in both image reconstruction and industrial downstream tasks. Our
code will be released at \url{https://github.com/jqtangust/FilmRemoval}.