Scene-based nonuniformity correction technique for infrared focal-plane arrays

Area of Science:

• Infrared imaging technology
• Sensor signal processing
• Image correction algorithms

Background:

• Infrared focal-plane array (IRFPA) sensors suffer from gain and bias nonuniformity, degrading image quality.
• Nonuniformity correction (NUC) is crucial for accurate IRFPA sensor performance.
• Existing NUC methods often struggle with blind-estimation challenges and temporal parameter drifts.

Purpose of the Study:

• To develop a scene-based nonuniformity correction (NUC) algorithm for IRFPA sensors.
• To address the challenges of blind estimation where scene values and detector parameters are unknown.
• To provide a robust NUC solution with low computational complexity and adaptability to parameter drifts.

Main Methods:

• Utilizes an interframe-prediction method to estimate the true scene.
• Employs a line-fitting technique to update gain and bias using estimated scene and observed data.
• Applies a simple formula for compensated output based on updated nonuniformity parameters.

Main Results:

• The proposed algorithm demonstrates low computational complexity and storage requirements.
• It effectively captures temporal drifts in nonuniformity parameters.
• Simulated and real infrared image sequence experiments show superior correction effects.

Conclusions:

• The scene-based NUC algorithm offers an effective solution for IRFPA nonuniformity.
• The method is computationally efficient and adaptable to changing sensor parameters.
• Experimental validation confirms its superior performance in infrared image correction.