# Polarization Guided Specular Reflection Separation

arXiv: https://arxiv.org/abs/2103.11652 · HTML: https://mlsanity.com/paper/2103.11652/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 69 references that are in the map.
- First posted on arXiv 22 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/2001.09322/) (2020)
- [Instance-aware Semantic Segmentation via Multi-task Network Cascades](https://mlsanity.com/paper/1512.04412/) (2015)
- [Depth from a polarisation + RGB stereo pair](https://mlsanity.com/paper/1903.12061/) (2019)
- [Fast and High Quality Highlight Removal from A Single Image](https://mlsanity.com/paper/1512.00237/) (2015)
- [A Sparse Representation Based Joint Demosaicing Method for Single-Chip Polarized Color Sensor](https://mlsanity.com/paper/1912.07308/) (2019)
- [Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning](https://mlsanity.com/paper/1310.5035/) (2013)

## Built on by (most cited first, 10 of 10)

- [Neural Spectro-polarimetric Fields](https://mlsanity.com/paper/2306.12562/) (2023)
- [Spectral and Polarization Vision: Spectro-polarimetric Real-world Dataset](https://mlsanity.com/paper/2311.17396/) (2023)
- [Learning to Remove Wrinkled Transparent Film with Polarized Prior](https://mlsanity.com/paper/2403.04368/) (2024)
- [ColonNeRF: High-Fidelity Neural Reconstruction of Long Colonoscopy](https://mlsanity.com/paper/2312.02015/) (2023)
- [Pola4All: survey of polarimetric applications and an open-source toolkit to analyze polarization](https://mlsanity.com/paper/2312.14697/) (2023)
- [PolarGS: Polarimetric Cues for Ambiguity-Free Gaussian Splatting with Accurate Geometry Recovery](https://mlsanity.com/paper/2512.00794/) (2025)
- [Highlight Specular Reflection Separation based on Tensor Low-rank and Sparse Decomposition Using Polarimetric Cues](https://mlsanity.com/paper/2207.03543/) (2022)
- [Single Document Image Highlight Removal via A Large-Scale Real-World Dataset and A Location-Aware Network](https://mlsanity.com/paper/2504.14238/) (2025)
- [Shift-Window Meets Dual Attention: A Multi-Model Architecture for Specular Highlight Removal](https://mlsanity.com/paper/2512.04496/) (2025)
- [PhyGaP: Physically-Grounded Gaussians with Polarization Cues](https://mlsanity.com/paper/2603.14001/) (2026)

## Abstract (arXiv)

Since specular reflection often exists in the real captured images and causes deviation between the recorded color and intrinsic color, specular reflection separation can bring advantages to multiple applications that require consistent object surface appearance. However, due to the color of an object is significantly influenced by the color of the illumination, the existing researches still suffer from the near-duplicate challenge, that is, the separation becomes unstable when the illumination color is close to the surface color. In this paper, we derive a polarization guided model to incorporate the polarization information into a designed iteration optimization separation strategy to separate the specular reflection. Based on the analysis of polarization, we propose a polarization guided model to generate a polarization chromaticity image, which is able to reveal the geometrical profile of the input image in complex scenarios, such as diversity of illumination. The polarization chromaticity image can accurately cluster the pixels with similar diffuse color. We further use the specular separation of all these clusters as an implicit prior to ensure that the diffuse components will not be mistakenly separated as the specular components. With the polarization guided model, we reformulate the specular reflection separation into a unified optimization function which can be solved by the ADMM strategy. The specular reflection will be detected and separated jointly by RGB and polarimetric information. Both qualitative and quantitative experimental results have shown that our method can faithfully separate the specular reflection, especially in some challenging scenarios.