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Structural Prior-Guided Weighted Low-Rank Denoising for Short-Wave Infrared Star Images.

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We developed a new method to remove noise from infrared star images, improving the detection of faint celestial objects. This technique enhances astronomical observations by preserving weak stellar targets and enabling real-time processing.

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Area of Science:

  • Astronomy and Astrophysics
  • Image Processing
  • Signal Processing

Background:

  • Ground-based astronomical observations, particularly in the short-wave infrared (SWIR) spectrum, are challenged by detector readout circuit temperature drift.
  • This drift causes nonlinear, non-uniform stripe noise and Gaussian noise, obscuring faint stellar targets and hindering detection.

Purpose of the Study:

  • To propose a novel denoising method for infrared star images that effectively suppresses structured noise while preserving weak stellar targets.
  • To address the limitations of traditional spatial filtering and standard low-rank decomposition methods in astronomical image processing.

Main Methods:

  • A structurally guided weighted low-rank denoising method integrating physical priors and mathematical optimization.
  • Utilizing point spread function (PSF) characteristics to create adaptive prior weights for preserving weak target energy.
  • Jointly modeling residual stripes and background as a low-rank component for separation.
  • Employing Bilateral Random Projection (BRP) to accelerate weighted soft-thresholding iterations.

Main Results:

  • The proposed method effectively suppresses structured stripe interference in real ground-based SWIR astronomical data.
  • Weak stellar targets are successfully preserved under low signal-to-noise ratio (SNR) conditions.
  • Ablation studies and sensitivity analyses confirm the method's efficacy and robustness.
  • The acceleration module significantly improves computational efficiency for practical, real-time processing.

Conclusions:

  • The developed method offers a significant advancement in denoising infrared star images, crucial for astronomical discovery.
  • It provides a robust framework for enhancing the detection of faint celestial objects in challenging observational conditions.
  • The integration of physical priors and accelerated optimization makes the method suitable for real-time astronomical applications.