Thanks to the latest progress in image sensor manufacturing technology, the emergence of the single-chip polarized color sensor is likely to bring advantages to computer vision tasks. Despite the importance of the sensor, joint chromatic and polarimetric demosaicing is the key to obtaining the high-quality RGB-Polarization image for the sensor. Since the polarized color sensor is equipped with a new type of chip, the demosaicing problem cannot be currently well-addressed by former methods. In this paper, we propose a joint chromatic and polarimetric demosaicing model to address this challenging problem. To solve this non-convex problem, we further present a sparse representation-based optimization strategy that utilizes chromatic information and polarimetric information to jointly optimize the model. In addition, we build an optical data acquisition system to collect an RGB-Polarization dataset. Results of both qualitative and quantitative experiments have shown that our method is capable of faithfully recovering full 12-channel chromatic and polarimetric information for each pixel from a single mosaic input image. Moreover, we show that the proposed method can perform well not only on the synthetic data but the real captured data.

中文翻译：

---

通过稀疏编码的联合色度和偏振去马赛克

得益于图像传感器制造技术的最新进展，单芯片偏振颜色传感器的出现很可能为计算机视觉任务带来优势。尽管传感器很重要，但联合色度和偏振去马赛克是为传感器获得高质量 RGB 偏振图像的关键。由于偏光颜色传感器配备了一种新型芯片，以前的方法目前无法很好地解决去马赛克问题。在本文中，我们提出了一种联合色度和极化去马赛克模型来解决这个具有挑战性的问题。为了解决这个非凸问题，我们进一步提出了一种基于稀疏表示的优化策略，该策略利用色度信息和极化信息来联合优化模型。此外，我们构建了一个光学数据采集系统来收集 RGB 偏振数据集。定性和定量实验的结果表明，我们的方法能够从单个马赛克输入图像中忠实地恢复每个像素的完整 12 通道色度和偏振信息。此外，我们表明所提出的方法不仅可以在合成数据上也可以在真实捕获的数据上表现良好。