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Research on Colorization Algorithm for γ-Photon Flow Field Images Using the SECN Model.

Hui Xiao1,2, Liying Hou1,2, Jiantang Liu1

  • 1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Road, Nanjing 211106, China.

Entropy (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

This study introduces a Structure Enhancement Colorization Network (SECN) to improve grayscale γ-photon tomography images for industrial flow monitoring. The SECN model effectively reduces color banding and enhances structural consistency, outperforming existing methods.

Keywords:
SECN modelflow field monitoringimage colorizationphoton imaging

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Area of Science:

  • Industrial imaging
  • Non-contact measurement techniques
  • Image processing

Background:

  • γ-photon tomography is a non-contact method for industrial flow monitoring.
  • Grayscale γ-photon images have statistical characteristics causing color banding with standard algorithms.
  • This compromises structural continuity and visual consistency in flow field analysis.

Purpose of the Study:

  • To develop an advanced colorization model for γ-photon flow-field images.
  • To address and mitigate color banding artifacts inherent in these images.
  • To enhance structural continuity and visual consistency for improved flow monitoring.

Main Methods:

  • Proposed a Structure Enhancement Colorization Network (SECN) using a U-Net + GAN framework with ResNet101.
  • Integrated structure-aware enhancement and multi-scale attention modules.
  • Enhanced the discriminator with improved boundary and texture discrimination blocks.

Main Results:

  • SECN achieved superior image quality metrics (PSNR, SSIM, FID, MAE) compared to DeOldify.
  • Demonstrated a 4.44% increase in information entropy, indicating better preservation of complex structures.
  • Achieved a significant 18.42% reduction in temperature inversion MAPE for parameter inversion.

Conclusions:

  • The SECN model effectively suppresses color banding artifacts in γ-photon flow-field images.
  • SECN enhances structural consistency and preserves crucial information for accurate flow monitoring.
  • The proposed method offers significant improvements for industrial applications relying on γ-photon tomography.