Chen Guangqiu, Hao Youfei, Duan Jin, Liu Ju, Jia Linfeng, Song Jingyuan
Electronics and Information Engineering Institute, Changchun University of Science and Technology, Changchun 130022, China.
Space Opto-Electronics Technology Institute, Changchun University of Science and Technology, Changchun 130022, China.
Sensors (Basel). 2024 May 22;24(11):3299. doi: 10.3390/s24113299.
Polarization imaging has achieved a wide range of applications in military and civilian fields such as camouflage detection and autonomous driving. However, when the imaging environment involves a low-light condition, the number of photons is low and the photon transmittance of the conventional Division-of-Focal-Plane (DoFP) structure is small. Therefore, the traditional demosaicing methods are often used to deal with the serious noise and distortion generated by polarization demosaicing in low-light environment. Based on the aforementioned issues, this paper proposes a model called Low-Light Sparse Polarization Demosaicing Network (LLSPD-Net) for simulating a sparse polarization sensor acquisition of polarization images in low-light environments. The model consists of two parts: an intensity image enhancement network and a Stokes vector complementation network. In this work, the intensity image enhancement network is used to enhance low-light images and obtain high-quality RGB images, while the Stokes vector is used to complement the network. We discard the traditional idea of polarization intensity image interpolation and instead design a polarization demosaicing method with Stokes vector complementation. By using the enhanced intensity image as a guide, the completion of the Stokes vector is achieved. In addition, to train our network, we collected a dataset of paired color polarization images that includes both low-light and regular-light conditions. A comparison with state-of-the-art methods on both self-constructed and publicly available datasets reveals that our model outperforms traditional low-light image enhancement demosaicing methods in both qualitative and quantitative experiments.
偏振成像已在伪装检测和自动驾驶等军事和民用领域获得了广泛应用。然而，当成像环境涉及低光照条件时，光子数量少且传统焦平面分割（DoFP）结构的光子透过率小。因此，传统的去马赛克方法常被用于处理低光照环境下偏振去马赛克产生的严重噪声和失真。基于上述问题，本文提出了一种名为低光照稀疏偏振去马赛克网络（LLSPD-Net）的模型，用于模拟低光照环境下偏振图像的稀疏偏振传感器采集。该模型由两部分组成：强度图像增强网络和斯托克斯矢量互补网络。在这项工作中，强度图像增强网络用于增强低光照图像并获得高质量的RGB图像，而斯托克斯矢量用于互补网络。我们摒弃了传统的偏振强度图像插值思路，转而设计了一种具有斯托克斯矢量互补的偏振去马赛克方法。通过使用增强后的强度图像作为引导，实现斯托克斯矢量的补全。此外，为了训练我们的网络，我们收集了一个包含低光照和正常光照条件的成对彩色偏振图像数据集。在自建数据集和公开可用数据集上与现有方法的比较表明，我们的模型在定性和定量实验中均优于传统的低光照图像增强去马赛克方法。
