The color division of focal plane (DoFP) polarization sensor structure mostly uses Bayer filter and polarization filter superimposed on each other, which makes the polarization imaging unsatisfactory in terms of photon transmission rate and information fidelity. In order to obtain high-resolution polarization images and high-quality RGB images simultaneously, we simulate a sparse division of focal plane polarization sensor structure, and seek a sweet spot of the simultaneous distribution of the Bayer filter and the polarization filters to obtain both high-resolution polarization images and high-quality RGB images. In addition, From the perspective of sparse polarization sensor imaging, leaving aside the traditional idea of polarization intensity interpolation, we propose a new sparse Stokes vector completion method, in which the network structure avoids the introduction and amplification of noise during polarization information acquisition by mapping the S₁ and S₂ components directly. The sparsely polarimetric image demosaicing (Sparse-PDM) model is a progressive combined structure of RGB image artifact removal enhancement network and sparsely polarimetric image completion network, which aims to compensate sparsely polarimetric Stokes parameter images with the de-artifacts RGB image as a guide, thus achieving high-quality polarization information and RGB image acquisition. Qualitative and quantitative experimental results on both self-constructed and publicly available datasets prove the superiority of our method over state-of-the-art methods.

Estimating the polarization properties of objects from polarization images is still an important but extremely undefined problem. Currently, there are two types of methods to probe the polarization properties of complex materials: one is about the equipment acquisition, which makes the collection of polarization information unsatisfactory due to the cumbersome equipment and intensive sampling, and the other is to use polarized imaging model for probing. Therefore, the accuracy of the polarized imaging model will be crucial. From an imaging perspective, we propose an end-to-end learning method that can predict accurate, physically based model parameters of polarimetric BRDF from a limited number of captured photographs of the object. In this work, we first design a novel pBRDF model as a powerful prior knowledge. This hybrid pBRDF model completely defines specular reflection, body scattering and directional diffuse reflection in imaging. Then, an end-to-end inverse rendering is performed to connect the multi-view measurements of the object with the geometry and pBRDF parameter estimation, and a reflectance gradient consistency loss is introduced to iteratively estimate the per-pixel normal, roughness, and polarimetric reflectance. Real-world measurement and rendering experiments show that the results obtained by applying our method are in strong agreement with ground truth, validating that we can reproduce the polarization properties of real-world objects using the estimated polarimetric reflectance.

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.

Scattering polarization properties are one of the main characteristics of a target. Different material types of the target surface can be expressed by the scattering polarization properties, and the use of polarization to characterise the target more effectively is a highly interesting topic. However, the existing polarized bidirectional reflection distribution function (pBRDF) based on microfacet theory for modelling light transmission over rough surfaces is not general. Modelling the scattering phenomena accurately on microsurfaces from a single specularly polarized reflection to full consideration of diffuse polarization remains a challenging task. In this work, we further introduce a directional diffuse reflection lobe to completely define the polarization properties of light in scattering, and revisit the microsurface in the masking and shadowing function part of the model, expanding the traditional symmetric V-groove structure, which is complemented by an asymmetric V-groove structure to improve the accuracy of the model description. We verify through a series of simulations and experiments that our model is in better agreement with the actual truth and that it is more suitable for describing the scattering polarization properties of most targets.