Optical image decomposition and noise filtering based on Laguerre-Gaussian modes

TL;DR

This paper introduces an efficient method for decomposing and denoising images using Laguerre-Gaussian modes, significantly reducing computation while maintaining high-quality reconstruction and noise filtering capabilities.

Contribution

The authors develop a novel computational approach for LG mode-based image decomposition that requires fewer sampling points, enhancing efficiency and enabling effective noise filtering.

Findings

Achieved high-fidelity image reconstruction with about 30,000 LG modes.

Demonstrated effective noise reduction through LG domain filtering.

Improved computational efficiency over existing methods.

Abstract

We propose and experimentally demonstrate an efficient image decomposition in the Laguerre-Gaussian (LG) domain. By developing an advanced computing method, the sampling points are much fewer than those in the existing methods, which can significantly improve the calculation efficiency. The beam waist, azimuthal and radial truncation orders of the LG modes are optimized depending on the image information to be restored. In the experiment, we decompose an image by using about 30000 LG modes and realize a high-fidelity reconstruction. Furthermore, we show image noise reduction through LG domain filtering. Our results open a door for LG-mode based image processing.