Evidence map›Paper›PMID 39384858›Full record

ArticleCommunications engineering2024

Fast autofocusing based on single-pixel moment detection.

Huiling Chen, Dongfeng Shi, Zijun Guo, Runbo Jiang, Linbin Zha, Yingjian Wang, Jan Flusser

Abstract read
In one paragraph

Article in Communications engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Huiling ChenSchool of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, 230026, China.
Dongfeng ShiSchool of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, 230026, China. dfshi@aiofm.ac.cn.ORCID http://orcid.org/0009-0005-3107-0233
Zijun GuoSchool of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, 230026, China.
Runbo JiangSchool of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, 230026, China.
Linbin ZhaSchool of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, 230026, China.
Yingjian WangSchool of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, 230026, China.
Jan FlusserCzech Academy of Sciences, Institute of Information Theory and Automation, Prague, Czech Republic.

Funding

České Vysoké Učení Technické v Praze (Czech Technical University in Prague) No. GA21-03921S
6 · The paper itself

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.

Identifiers

PMID39384858
PMCPMC11479630

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.