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Visible-Image-Assisted Nonuniformity Correction of Infrared Images Using the GAN with SEBlock.

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Summary

This study introduces a visible-image-assisted nonuniformity correction (NUC) method using a dual-discriminator generative adversarial network (GAN). The novel approach effectively reduces image detail loss and edge blur in infrared images.

Keywords:
generative adversarial networkinfrared imagenonuniformity correctionvisible imagevision transformer

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Existing nonuniformity correction (NUC) methods often suffer from image detail loss and edge blur.
  • Developing advanced NUC techniques is crucial for accurate infrared image analysis.

Purpose of the Study:

  • To propose a novel visible-image-assisted NUC algorithm (VIA-NUC) that minimizes image detail loss and edge blur.
  • To enhance the uniformity of infrared images using a generative adversarial network (GAN) framework.

Main Methods:

  • A dual-discriminator GAN with SEBlock was developed for NUC, utilizing visible images as a reference.
  • Multiscale feature extraction was performed on infrared and visible images separately.
  • Image reconstruction involved decoding infrared features with assistance from corresponding visible features, incorporating SEBlock and skip connections.
  • Two discriminators, based on Vision Transformer (Vit) and Discrete Wavelet Transform (DWT), provided global and local judgments for adversarial learning.

Main Results:

  • The VIA-NUC method effectively removed nonuniform noise while preserving image texture.
  • The corrected images achieved an average Structural Similarity (SSIM) exceeding 0.97 and an average Peak Signal-to-Noise Ratio (PSNR) above 37.11 dB.
  • The proposed method demonstrated a performance improvement of over 3% in metric evaluations compared to existing methods.

Conclusions:

  • The proposed visible-image-assisted NUC algorithm significantly improves image quality by reducing detail loss and edge blur.
  • The dual-discriminator GAN with SEBlock provides a robust framework for accurate and effective nonuniformity correction in infrared imaging.
  • The method shows strong potential for applications requiring high-fidelity infrared image uniformity.