Efficient and accurate conversion-gain estimation of a photon-counting image sensor based on the maximum likelihood...

Area of Science:

• Image Sensor Technology
• Statistical Signal Processing
• Photon Counting Imaging

Background:

• Accurate estimation of conversion gain is crucial for quantitative image sensor analysis.
• Existing methods for conversion gain estimation often require substantial data accumulation.
• CMOS image sensors capable of photon counting offer new possibilities for sensor characterization.

Purpose of the Study:

• To establish a novel, data-efficient method for estimating image sensor conversion gains.
• To leverage maximum likelihood estimation for improved accuracy in gain determination.
• To validate the method using both numerical simulations and experimental photon-counting data.

Main Methods:

• Development of a conversion gain estimation algorithm based on maximum likelihood estimation.
• Numerical simulations to assess accuracy and data requirements compared to known methods.
• Application of the method to experimental images from a photon-counting CMOS image sensor.

Main Results:

• Numerical simulations demonstrated superior accuracy and reduced data needs for the proposed method.
• Experimental validation achieved three-digit accuracy in conversion gain determination.
• The method required up to 10 times less data than conventional approaches for similar accuracy.

Conclusions:

• The proposed maximum likelihood estimation method provides a highly accurate and data-efficient approach for image sensor conversion gain estimation.
• This technique is particularly effective for photon-counting sensors, enabling precise characterization with minimal data.
• The findings offer a significant advancement for image sensor calibration and performance evaluation.