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}.

翻译：本文研究了一个新问题——薄膜去除（Film Removal, FR），旨在去除起皱透明薄膜的干扰，并恢复薄膜下的原始信息，以服务于工业识别系统。我们首先对覆盖有薄膜的工业材料成像过程进行了物理建模。考虑到偏振相机能有效记录薄膜产生的镜面高光，我们构建了一个包含偏振信息的实用数据集，其中包含有薄膜和无薄膜的成对数据。我们尝试通过端到端框架去除薄膜的干扰（包括镜面高光及其他退化）。为了定位镜面高光区域，我们使用角度估计网络优化偏振角，以最小化镜面高光。将镜面高光最小化后的图像作为先验，以支持重建网络。基于该先验和偏振图像，重建网络能够解耦薄膜引起的所有退化。大量实验表明，我们的框架在图像重建和工业下游任务中均达到了最优性能（SOTA）。代码将在\url{https://github.com/jqtangust/FilmRemoval}发布。