Fast autofocusing based on single-pixel moment detection

Traditional image processing-based autofocusing techniques require the acquisition, storage, and processing of large amounts of image sequences, constraining focusing speed and cost. Here we propose an autofocusing technique, which directly and exactly acquires the geometric moments of the target object in real time at different locations by means of a proper image modulation and detection by a single-pixel detector. An autofocusing criterion is then formulated using the central moments, and the fast acquisition of the focal point is achieved by searching for the position that minimizes the criterion. Theoretical analysis and experimental validation of the method are performed and the results show that the method can achieve fast and accurate autofocusing. The proposed method requires only three single-pixel detections for each focusing position of the target object to evaluate the focusing criterion without imaging the target object. The method does not require any active object-to-camera distance measurement. Comparing to local differential methods such as contrast or gradient measurement, our method is more stable to noise and requires very little data compared with the traditional image processing methods. It may find a wide range of potential applications and prospects, particularly in low-light imaging and near-infra imaging, where the level of noise is typically high.

Authors

Institutions

Publication Details

Journal
Communications Engineering
Published
2024-10-09
DOI
https://doi.org/10.1038/s44172-024-00288-z
Citations
3
Primary Topic
Image Processing Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.96

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fast autofocusing based on single-pixel moment detection

Runbo Jiang, Huiling Chen, Jan Flusser, Linbin Zha et al.
3 citations
Communications Engineering
Image Processing Techniques and Applications
0.96
article

Fast autofocusing based on single-pixel moment detection

Runbo Jiang, Huiling Chen, Jan Flusser, Linbin Zha, Dongfeng Shi, Zijun Guo, Yingjian Wang
article en
3 citations

Abstract

Traditional image processing-based autofocusing techniques require the acquisition, storage, and processing of large amounts of image sequences, constraining focusing speed and cost. Here we propose an autofocusing technique, which directly and exactly acquires the geometric moments of the target object in real time at different locations by means of a proper image modulation and detection by a single-pixel detector. An autofocusing criterion is then formulated using the central moments, and the fast acquisition of the focal point is achieved by searching for the position that minimizes the criterion. Theoretical analysis and experimental validation of the method are performed and the results show that the method can achieve fast and accurate autofocusing. The proposed method requires only three single-pixel detections for each focusing position of the target object to evaluate the focusing criterion without imaging the target object. The method does not require any active object-to-camera distance measurement. Comparing to local differential methods such as contrast or gradient measurement, our method is more stable to noise and requires very little data compared with the traditional image processing methods. It may find a wide range of potential applications and prospects, particularly in low-light imaging and near-infra imaging, where the level of noise is typically high.

Communications EngineeringVol. 3(1)
University of Science and Technology of China (CN), Chinese Academy of Sciences (CN), Hefei Institutes of Physical Science (CN), Czech Academy of Sciences, Institute of Information Theory and Automation (CZ), Anhui Institute of Optics and Fine Mechanics (CN), Advanced Laser Technology (United Kingdom) (GB)
České Vysoké Učení Technické v Praze, Grantová Agentura České Republiky, Chinese Academy of Sciences, Youth Innovation Promotion Association of the Chinese Academy of Sciences, Youth Innovation Promotion Association
Openalex Percentile: Top 21%
Image Processing Techniques and Applications
0.96
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.