Optimal biasing and physical limits of DVS event noise
Under dim lighting conditions, the output of Dynamic Vision Sensor (DVS) event cameras is strongly affected by noise. Photon and electron shot-noise cause a high rate of non-informative events that reduce Signal to Noise ratio. DVS noise performance depends not only on the scene illumination, but also on the user-controllable biasing of the camera. In this paper, we explore the physical limits of DVS noise, showing that the DVS photoreceptor is limited to a theoretical minimum of 2x photon shot noise, and we discuss how biasing the DVS with high photoreceptor bias and adequate source-follower bias approaches optimal noise performance. We support our conclusions with pixel-level measurements of a DAVIS346 and analysis of a theoretical pixel model.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Towards Debiasing NLU Models from Unknown Biases
NLU models often exploit biases to achieve high dataset-specific performance without properly learning the intended task. Recently proposed debiasing methods are shown to be effective in mitigating this tendency. However…
Statistical Inference in Tensor Completion: Optimal Uncertainty Quantification and Statistical-to-Computational Gaps
This paper presents a simple yet efficient method for statistical inference of tensor linear forms using incomplete and noisy observations. Under the Tucker low-rank tensor model and the missing-at-random assumption, we …
Uncertainty QuantificationThe Fundamental Limits of Structure-Agnostic Functional Estimation
Many recent developments in causal inference, and functional estimation problems more generally, have been motivated by the fact that classical one-step (first-order) debiasing methods, or their more recent sample-split …
Causal InferenceStable Natural Language Understanding via Invariant Causal Constraint
Natural Language Understanding (NLU) task requires the model to understand the underlying semantics of input text. However, recent analyses demonstrate that NLU models tend to utilize dataset biases to achieve high datas…
Natural Language UnderstandingToward Optimal ANC: Establishing Mutual Information Lower Bound
Active Noise Cancellation (ANC) algorithms aim to suppress unwanted acoustic disturbances by generating anti-noise signals that destructively interfere with the original noise in real time. Although recent deep learning-…