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This study introduces a novel method using conditional generative adversarial networks (CGAN) to correct nonuniformity in space images. The technique effectively removes background noise, enhancing image quality for astronomical observations.

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Area of Science:

  • Astronomy
  • Image Processing
  • Artificial Intelligence

Background:

  • Ground-based telescopes face challenges like vignetting and detector nonuniformity.
  • These issues degrade the quality of acquired space images.

Purpose of the Study:

  • To develop an effective method for correcting space image nonuniformity.
  • To improve the signal-to-noise ratio (SNR) and accuracy of astronomical images.

Main Methods:

  • A conditional generative adversarial network (CGAN) was developed for nonuniformity correction.
  • A training dataset was created using a physical vignetting model and simulated nonuniform backgrounds.
  • The generator network within the CGAN was enhanced for improved performance.

Main Results:

  • The proposed CGAN method effectively removed nonuniform backgrounds from simulated and real space images.
  • Achieved a Mean Square Error (MSE) of 4.56 on the simulation dataset.
  • Improved the signal-to-noise ratio (SNR) by 43.87% for real space images.

Conclusions:

  • The CGAN-based method offers a robust solution for space image nonuniformity correction.
  • This technique enhances the quality and reliability of astronomical data acquisition.