# Hyperspectral phase imaging based on denoising in complex-valued eigensubspace

## TL;DR

This paper introduces a novel denoising algorithm for hyperspectral complex data that leverages eigenspace techniques and 3D filtering, demonstrating improved accuracy and noise resilience in simulations and experiments.

## Contribution

The paper presents a new hyperspectral denoising method based on complex eigenspace and 3D Wiener filtering, enhancing noise reduction in complex-valued data.

## Key findings

- Effective noise suppression in highly noisy data
- Reliable results demonstrated in simulations and experiments
- Improved quantitative accuracy over existing methods

## Abstract

A new denoising algorithm for hyperspectral complex domain data has been developed and studied. This algorithm is based on the complex domain block-matching 3D filter including the 3D Wiener filtering stage. The developed algorithm is applied and tuned to work in the singular value decomposition (SVD) eigenspace of reduced dimension. The accuracy and quantitative advantage of the new algorithm are demonstrated in simulation tests and in the processing of the experimental data. It is shown that the algorithm is effective and provides reliable results even for highly noisy data.