# Polarization Denoising and Demosaicking: Dataset and Baseline Method

arXiv: https://arxiv.org/abs/2509.10098 · HTML: https://mlsanity.com/paper/2509.10098/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 12 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [Polarized Reflection Removal with Perfect Alignment in the Wild](https://mlsanity.com/paper/2003.12789/) (2020)
- [Two-Step Color-Polarization Demosaicking Network](https://mlsanity.com/paper/2209.06027/) (2022)
- [Denoising Imaging Polarimetry by an Adapted BM3D Method](https://mlsanity.com/paper/1711.04853/) (2017)
- [Embedded polarizing filters to separate diffuse and specular reflection](https://mlsanity.com/paper/1811.02608/) (2018)

## Built on by (most cited first, 5 of 5)

- [Stokes-Informed Diffusion for Robust Linear Polarization Estimation](https://mlsanity.com/paper/2607.21239/) (2026)
- [PolarVSR: A Unified Framework and Benchmark for Continuous Space-Time Polarization Video Reconstruction](https://mlsanity.com/paper/2605.10275/) (2026)
- [CPDDNet: Color-Polarization Denoising and Demosaicking Network](https://mlsanity.com/paper/2607.01100/) (2026)
- [DensePol: Dense-Angle Polarization Dataset for Learning-Based Polarimetric Vision](https://mlsanity.com/paper/2609.09359/) (2026)
- [Learning Polarization Image Restoration with General Restoration Priors](https://mlsanity.com/paper/2609.30728/) (2026)

## Abstract (arXiv)

A division-of-focal-plane (DoFP) polarimeter enables us to acquire images with multiple polarization orientations in one shot and thus it is valuable for many applications using polarimetric information. The image processing pipeline for a DoFP polarimeter entails two crucial tasks: denoising and demosaicking. While polarization demosaicking for a noise-free case has increasingly been studied, the research for the joint task of polarization denoising and demosaicking is scarce due to the lack of a suitable evaluation dataset and a solid baseline method. In this paper, we propose a novel dataset and method for polarization denoising and demosaicking. Our dataset contains 40 real-world scenes and three noise-level conditions, consisting of pairs of noisy mosaic inputs and noise-free full images. Our method takes a denoising-then-demosaicking approach based on well-accepted signal processing components to offer a reproducible method. Experimental results demonstrate that our method exhibits higher image reconstruction performance than other alternative methods, offering a solid baseline.