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Related Experiment Video

Updated: Jun 5, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System
10:09

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

Adaptive convergence nonuniformity correction algorithm.

Weixian Qian1, Qian Chen, Junqi Bai

  • 1440 Lab, JGMT, EEOT, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, China, 210094. developer_plus@163.com

Applied Optics
|January 12, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces space frequency to nonuniformity correction (NUC) algorithms, improving convergence speed and stability. The new method reduces artifacts in infrared images, enhancing overall image quality.

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Last Updated: Jun 5, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System
10:09

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

Area of Science:

  • Optics and Photonics
  • Image Processing
  • Signal Processing

Background:

  • Scene-based nonuniformity correction (NUC) algorithms commonly face challenges with convergence and ghosting artifacts.
  • Existing NUC methods struggle to adapt to dynamic scene changes, impacting correction accuracy.

Purpose of the Study:

  • To introduce space frequency concepts into scene-based NUC algorithms.
  • To develop an adaptive convergence speed factor for improved NUC performance.
  • To enhance the stability and effectiveness of NUC algorithms in infrared imaging.

Main Methods:

  • Incorporation of space frequency analysis into the NUC framework.
  • Development and application of an adaptive convergence speed factor based on scene dynamic range.
  • Utilizing experimental statistical data to summarize and correct for nonuniformity space relativity.
  • Testing the algorithm on real and simulated infrared image sequences.

Main Results:

  • The proposed convergence speed factor adaptively adjusts correction speed based on scene dynamic range.
  • The algorithm effectively decreases statistical data standard deviation, reducing artifacts.
  • Space relativity characteristics were identified and used to stabilize the convergence speed factor.
  • Demonstrated positive effects on real and simulated infrared image sequences.

Conclusions:

  • The integration of space frequency significantly enhances scene-based NUC algorithms.
  • The adaptive convergence speed factor improves correction efficiency and stability.
  • The developed algorithm effectively mitigates convergence and ghosting artifacts in infrared imaging.