Unraveling the paradox of intensity-dependent DVS pixel noise
Dynamic vision sensor (DVS) event camera output is affected by noise, particularly in dim lighting conditions. A theory explaining how photon and electron noise affect DVS output events has so far not been developed. Moreover, there is no clear understanding of how DVS parameters and operating conditions affect noise. There is an apparent paradox between the real noise data observed from the DVS output and the reported noise measurements of the logarithmic photoreceptor. While measurements of the logarithmic photoreceptor predict that the photoreceptor is approximately a first-order system with RMS noise voltage independent of the photocurrent, DVS output shows higher noise event rates at low light intensity. This paper unravels this paradox by showing how the DVS photoreceptor is a second-order system, and the assumption that it is first-order is generally not reasonable. As we show, at higher photocurrents, the photoreceptor amplifier dominates the frequency response, causing a drop in RMS noise voltage and noise event rate. We bring light to the noise performance of the DVS photoreceptor by presenting a theoretical explanation supported by both transistor-level simulation results and chip measurements.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Practical Noise Modeling for SPAD Intensity Imaging
Single-photon avalanche diode (SPAD) cameras are promising for low-light and high-dynamic-range intensity imaging, but their practical use is limited by complex sensor-specific noise. Unlike time-correlated single-photon…
v2e: From Video Frames to Realistic DVS Events
To help meet the increasing need for dynamic vision sensor (DVS) event camera data, this paper proposes the v2e toolbox that generates realistic synthetic DVS events from intensity frames. It also clarifies incorrect cla…
Object RecognitionEmbedding Textual Information in Images Using Quinary Pixel Combinations
This paper presents a novel technique for embedding textual data into images using quinary combinations of pixel intensities in RGB space. Existing methods predominantly rely on least and most significant bit (LSB & MSB)…
Divide and Conquer: Heterogeneous Noise Integration for Diffusion-based Adversarial Purification
Existing diffusion-based purification methods aim to disrupt adversarial perturbations by introducing a certain amount of noise through a forward diffusion process, followed by a reverse process to recover clean exam…
Adversarial PurificationEnd-to-End QGAN-Based Image Synthesis via Neural Noise Encoding and Intensity Calibration
Quantum Generative Adversarial Networks (QGANs) offer a promising path for learning data distributions on near-term quantum devices. However, existing QGANs for image synthesis avoid direct full-image generation, relying…
Image Generation