Fixed-Pattern Noise Modeling and Removal in Time-of-Flight Sensing

Abstract

In this paper, we discuss the modeling and removal of fixed-pattern noise (FPN) in photonic mixture devices employing the time-of-flight (ToF) principle for range measurements and scene depth estimation. We present a case that arises from low-sensing (LS) conditions caused by either external factors related to scene reflectivity or internal factors related to the power and operation mode of the sensor or both. In such a case, the FPN becomes especially dominating and invalidates previously adopted noise models, which have been used for removal of other noise contaminations in ToF measurements. To tackle LS cases, we propose a noise model specifically addressing the presence of FPN and develop a relevant FPN removal procedure. We demonstrate, by experiments with synthetic and real-world data, that the proper modeling and removing of FPN is substantial for the subsequent Gaussian denoising and yields accurate depth maps comparable to the ones obtainable in normal operating mode.