# Denoising Imaging Polarimetry by an Adapted BM3D Method

arXiv: https://arxiv.org/abs/1711.04853 · HTML: https://mlsanity.com/paper/1711.04853/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Simultaneous Acquisition of High Quality RGB Image and Polarization Information using a Sparse Polarization Sensor](https://mlsanity.com/paper/2209.13106/) (2022)
- [Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging](https://mlsanity.com/paper/2601.23276/) (2026)
- [Learning to Deblur Polarized Images](https://mlsanity.com/paper/2402.18134/) (2024)
- [Polarization Denoising and Demosaicking: Dataset and Baseline Method](https://mlsanity.com/paper/2509.10098/) (2025)

## Abstract (arXiv)

Imaging polarimetry allows more information to be extracted from a scene than conventional intensity or colour imaging. However, a major challenge of imaging polarimetry is image degradation due to noise. This paper investigates the mitigation of noise through denoising algorithms and compares existing denoising algorithms with a new method, based on BM3D. This algorithm, PBM3D, gives visual quality superior to the state of the art across all images and noise standard deviations tested. We show that denoising polarization images using PBM3D allows the degree of polarization to be more accurately calculated by comparing it to spectroscopy methods.