Denoising Imaging Polarimetry by an Adapted BM3D Method

TL;DR

This paper introduces PBM3D, a denoising algorithm for imaging polarimetry that outperforms existing methods, enabling more accurate polarization measurements from noisy images.

Contribution

The paper presents PBM3D, a novel adaptation of BM3D specifically designed for polarimetric image denoising, improving visual quality and measurement accuracy.

Findings

PBM3D outperforms existing denoising algorithms across various images and noise levels.

Denoising with PBM3D improves the accuracy of polarization degree calculations.

PBM3D enhances the quality of polarimetric images, facilitating better scientific analysis.

Abstract

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.