Image quality metric parameter for holographic modal wavefront sensing

In this Letter, we introduce a novel, to the best of our knowledge, image quality metric parameter (QMP) derived from the spatial intensity distribution co-occurrence matrix for an improved holographic modal wavefront sensing (HMWS). The proposed QMP overcomes a number of serious limitations associated with the conventional approach, including reliance on the location of the focal point spread function, detector aperture diameter, and biased values. The viability and significance of the proposed QMP are amply proved by the simulation and experimental results, which are obtained both with the standard HMWS approach and with the convolutional neural network.

Authors

Publication Details

Journal
Optics Letters
Published
2026-04-06
DOI
https://doi.org/10.1364/ol.591080
Primary Topic
Adaptive optics and wavefront sensing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Image quality metric parameter for holographic modal wavefront sensing

Biswajit Pathak, Dinesh N. Naik, Anitta Jomy, Aryan Chand
Optics Letters
Adaptive optics and wavefront sensing
article

Image quality metric parameter for holographic modal wavefront sensing

Biswajit Pathak, Dinesh N. Naik, Anitta Jomy, Aryan Chand
article en

Abstract

In this Letter, we introduce a novel, to the best of our knowledge, image quality metric parameter (QMP) derived from the spatial intensity distribution co-occurrence matrix for an improved holographic modal wavefront sensing (HMWS). The proposed QMP overcomes a number of serious limitations associated with the conventional approach, including reliance on the location of the focal point spread function, detector aperture diameter, and biased values. The viability and significance of the proposed QMP are amply proved by the simulation and experimental results, which are obtained both with the standard HMWS approach and with the convolutional neural network.

Optics LettersVol. 51(9)
Openalex Percentile: Top 24%
Adaptive optics and wavefront sensing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.