TY - GEN
T1 - Dark Count Removal in Photon-Counting SPAD Arrays
AU - Peretti, Edoardo
AU - Suonsivu, Aleksi
AU - Salmela, Lauri
AU - Uosukainen, Leevi
AU - Bilcu, Radu Ciprian
AU - Boracchi, Giacomo
PY - 2025
Y1 - 2025
N2 - Single-Photon Avalanche Diodes (SPADs) are an emerging pixel technology able to detect the arrival of single photons. Arrays of SPADs can be used to image a scene in binary frames, indicating whether there has been a photon detection in a pixel during a very short frame exposure (e.g., few microseconds). Like many imaging technologies, SPAD sensors suffer from false photon hits, called Dark Count (DC), which increase the photon count, resulting in abnormally bright pixels, called hot pixels. Correction methods used for common CCD/CMOS sensors, such as dark frame subtraction, are ineffective for SPADs, which have non-linear response in observed counts due to their 1-bit quantization. Thus, we derive an analytical model of the count bias introduced by DC, which is signal-dependent, and provide a pixel-wise, closed-form DC correction algorithm. Remarkably, our correction is optimal in expectation, meaning that on average it removes perfectly the distortion. We validate our method on real SPAD acquisitions and synthetic data, showing that it significantly improves image reconstruction.
AB - Single-Photon Avalanche Diodes (SPADs) are an emerging pixel technology able to detect the arrival of single photons. Arrays of SPADs can be used to image a scene in binary frames, indicating whether there has been a photon detection in a pixel during a very short frame exposure (e.g., few microseconds). Like many imaging technologies, SPAD sensors suffer from false photon hits, called Dark Count (DC), which increase the photon count, resulting in abnormally bright pixels, called hot pixels. Correction methods used for common CCD/CMOS sensors, such as dark frame subtraction, are ineffective for SPADs, which have non-linear response in observed counts due to their 1-bit quantization. Thus, we derive an analytical model of the count bias introduced by DC, which is signal-dependent, and provide a pixel-wise, closed-form DC correction algorithm. Remarkably, our correction is optimal in expectation, meaning that on average it removes perfectly the distortion. We validate our method on real SPAD acquisitions and synthetic data, showing that it significantly improves image reconstruction.
U2 - 10.1109/ICIP55913.2025.11084612
DO - 10.1109/ICIP55913.2025.11084612
M3 - Conference contribution
T3 - Proceedings - International Conference on Image Processing
SP - 522
EP - 527
BT - 2025 IEEE International Conference on Image Processing (ICIP)
PB - IEEE
T2 - IEEE International Conference on Image Processing Workshops
Y2 - 14 September 2025 through 17 September 2025
ER -