Dynamic mutation enhanced greedy strategy for wavefront shaping

TL;DR

This paper introduces a mutate greedy algorithm for wavefront shaping that improves optical focusing through scattering media by combining greedy strategies with real-time mutation feedback, achieving high enhancement and fast convergence.

Contribution

It proposes a novel mutate greedy algorithm that overcomes limitations of existing population optimization methods in wavefront shaping, eliminating the need for parameter tuning.

Findings

Achieves high enhancement in wavefront shaping

Ensures fast convergence without parameter tuning

Outperforms traditional population algorithms

Abstract

Optical focusing through scattering media has important implications for optical applications in medicine, communications, and detection. In recent years, many wavefront shaping methods have been successfully applied to the field, among which the population optimization algorithm has achieved remarkable results. However, the current population optimization algorithm has some drawbacks: 1. the offspring do not fully inherit the good genes from the parent. 2. more efforts are needed to tune the parameters. In this paper, we propose the mutate greedy algorithm. It combines greedy strategies and real-time feedback of mutation rates to generate offspring. In wavefront shaping, people can realize high enhancement and fast convergence without a parameter-tuning process.