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 https://github.com/jqtangust/FilmRemoval.

本研究旨在研究一个新的问题，即去除褶皱透明薄膜的干扰并在工业识别系统中重建薄膜下的原始信息。通过建立物理模型，我们使用偏振摄像机构建了一个实际数据集，旨在去除薄膜中的干扰。我们使用角度估计网络定位镜面高光，并通过重建网络解耦薄膜中的所有降解物。实验证明，我们的框架在图像重建和工业下游任务中取得了 SOTA 性能。