Polarization cameras can capture multiple polarized images with different polarizer angles in a single shot, bringing convenience to polarization-based downstream tasks. However, their direct outputs are color-polarization filter array (CPFA) raw images, requiring demosaicing to reconstruct full-resolution, full-color polarized images; unfortunately, this necessary step introduces artifacts that make polarization-related parameters such as the degree of polarization (DoP) and angle of polarization (AoP) prone to error. Besides, limited by the hardware design, the resolution of a polarization camera is often much lower than that of a conventional RGB camera. Existing polarized image demosaicing (PID) methods are limited in that they cannot enhance resolution, while polarized image super-resolution (PISR) methods, though designed to obtain high-resolution (HR) polarized images from the demosaicing results, tend to retain or even amplify errors in the DoP and AoP introduced by demosaicing artifacts. In this paper, we propose PIDSR, a joint framework that performs complementary Polarized Image Demosaicing and Super-Resolution, showing the ability to robustly obtain high-quality HR polarized images with more accurate DoP and AoP from a CPFA raw image in a direct manner. Experiments show our PIDSR not only achieves state-of-the-art performance on both synthetic and real data, but also facilitates downstream tasks.

Polarization images facilitate image enhancement and 3D reconstruction tasks, but the limited accessibility of polarization cameras hinders their broader application. This gap drives the need for synthesizing photorealistic polarization images. The existing polarization simulator Mitsuba relies on a parametric polarization image formation model and requires extensive 3D assets covering shape and PBR materials, preventing it from generating large-scale photorealistic images. To address this problem, we propose PolarAnything, capable of synthesizing polarization images from a single RGB input with both photorealism and physical accuracy, eliminating the dependency on 3D asset collections. Drawing inspiration from the zero-shot performance of pretrained diffusion models, we introduce a diffusion-based generative framework with an effective representation strategy that preserves the fidelity of polarization properties. Experiments show that our model generates high-quality polarization images and supports downstream tasks like shape from polarization.

Shape from polarization (SfP) benefits from advancements like polarization cameras for single-shot normal estimation, but its performance heavily relies on light conditions. This paper proposes SfPUEL, an end-to-end SfP method to jointly estimate surface normal and material under unknown environment light. To handle this challenging light condition, we design a transformer-based framework for enhancing the perception of global context features. We further propose to integrate photometric stereo (PS) priors from pretrained models to enrich extracted features for high-quality normal predictions. As metallic and dielectric materials exhibit different BRDFs, SfPUEL additionally predicts dielectric and metallic material segmentation to further boost performance. Experimental results on synthetic and our collected real-world dataset demonstrate that SfPUEL significantly outperforms existing SfP and single-shot normal estimation methods. The code and dataset is available at https://github.com/YouweiLyu/SfPUEL.

Polarization-based vision algorithms have found uses in various applications since polarization provides additional physical constraints. However, in low-light conditions, their performance would be severely degenerated since the captured polarized images could be noisy, leading to noticeable degradation in the degree of polarization (DoP) and the angle of polarization (AoP). Existing low-light image enhancement methods cannot handle the polarized images well since they operate in the intensity domain, without effectively exploiting the information provided by polarization. In this paper, we propose a Stokes-domain enhancement pipeline along with a dual-branch neural network to handle the problem in a polarization-aware manner. Two application scenarios (reflection removal and shape from polarization) are presented to show how our enhancement can improve their results.

Undesirable reflections contained in photos taken in front of glass windows or doors often degrade visual quality of the image. Separating two layers apart benefits both human and machine perception. The polarization status of the light changes after refraction or reflection, providing more observations of the scene, which can benefit the reflection separation. Different from previous works that take three or more polarization images as input, we propose to exploit physical constraints from a pair of unpolarized and polarized images to separate reflection and transmission layers in this paper. Due to the simplified capturing setup, the system is more under-determined compared to the existing polarization-based works. In order to solve this problem, we propose to estimate the semi-reflector orientation first to make the physical image formation well-posed, and then learn to reliably separate two layers using additional networks based on both physical and numerical analysis. In addition, a motion estimation network is introduced to handle the misalignment of paired input. Quantitative and qualitative experimental results show our approach performs favorably over existing polarization and single image based solutions.

This article presents a new approach for surface normal recovery from polarization images under an unknown distant light. Polarization provides rich cues of object geometry and material, but it is also influenced by different lighting conditions. Different from previous Shape-from-Polarization (SfP) methods, which rely on handcrafted or data-driven priors, we analytically investigate the benefits of estimating distant lighting for resolving the ambiguity in normal estimation from SfP using the polarimetric Bidirectional Reflectance Distribution Function (pBRDF) based image formation model. We then propose a two-stage learning framework that first effectively exploits polarization and shading cues to estimate the reflectance and lighting information and then optimizes the initial normal as the geometric prior. Leveraging the normal prior with the polarization cues from the input images, our network further generates the surface normal with more details in the second stage. We also present a data generation pipeline derived from the pBRDF model enabling model training and create a real dataset for evaluation of SfP approaches. Extensive ablation studies show the effectiveness of our designed architecture, and our approach outperforms existing methods in quantitative and qualitative experiments on real data.

Removing undesirable reflections in photographs benefits both human perceptions and downstream computer vision tasks, but it is a highly ill-posed problem based on a single RGB image. Different from RGB images, near-infrared (NIR) images captured by an active NIR camera are less likely to be affected by reflections when glass and camera planes form certain angles, while textures on objects could “vanish” in some situations. Based on this observation, we propose a cascaded reflection removal network with an image feature fusion strategy to utilize auxiliary information in active NIR images. To tackle the insufficiency of training data, we propose a data generation pipeline to approximate perceptual properties and the reflection-suppressing nature of active NIR images. We further build a dataset with synthetic and real images to facilitate the research. Experimental results show that the proposed method outperforms state-of-the-art reflection removal methods in both quantitative metrics and visual quality.

Removing reflections from a single RGB image is a highly ill-posed problem. Unlike RGB images, near-infrared (NIR) images obtained through an active NIR camera are less likely to be affected by reflections when glass and camera planes form certain angles, while textures on objects could “vanish” under certain circumstances. Based on this observation, we propose a two-stream neural network to remove undesired reflections in an RGB image with the guidance of an NIR image. To tackle the insufficiency of training data, we propose a synthetic data generation pipeline that simulates the reflection-suppressing nature of the active NIR imaging and build a dataset mixed with synthetic and real data. Experimental results show that the proposed method outperforms state-of-the-art reflection removal methods in both quantitative metrics and visual quality.

When we take photos through glass windows or doors, the transmitted background scene is often blended with undesirable reflection. Separating two layers apart to enhance the image quality is of vital importance for both human and machine perception. In this paper, we propose to exploit physical constraints from a pair of unpolarized and polarized images to separate reflection and transmission layers. Due to the simplified capturing setup, the system becomes more underdetermined compared with existing polarization based solutions that take three or more images as input. We propose to solve semireflector orientation estimation first to make the physical image formation well-posed and then learn to reliably separate two layers using a refinement network with gradient loss. Quantitative and qualitative experimental results show our approach performs favorably over existing polarization and single image based solutions.