
LED flicker is a persistent artifact in imaging, where lights modulated via Pulse Width Modulation (PWM) above 90 Hz appear steady to humans but produce temporal intensity variations in captured video. While hardware mitigations like split-pixel architectures reduce flicker, they introduce a fundamental trade-off with motion blur. Progress in learned LED flicker mitigation (LFM) is currently hindered by a lack of public ground-truth datasets. We address this gap with ISET-LFM, an open-source physics-based simulation framework that models LED flicker in driving scenes. Built on the ISET ecosystem, our pipelinecombines camera motion simulation with an analytical flicker model to generate realistic dual-exposure frame sequences alongsideflicker-free ground truth. We provide a synthetic datasetof scene radiance, enabling benchmarking and training of LFMalgorithms across diverse sensor and ISP architectures. Thecode and dataset are available at: https: // github. com/ AyushJam/ iset-lfm and https: // purl. stanford. edu/ wd776hn7919 respectively.

In recent years, the use of LED lighting has become widespread in the automotive environment, largely because of their high energy efficiency, reliability, and low maintenance costs. There has also been a concurrent increase in the use and complexity of automotive camera systems. To a large extent, LED lighting and automotive camera technology evolved separately and independently. As the use of both technologies has increased, it has become clear that LED lighting poses significant challenges for automotive imaging i.e. so-called "LED flicker". LED flicker is an artifact observed in digital imaging where an imaged light source appears to flicker, even though the light source appears constant to a human observer. This paper defines the root cause and manifestations of LED flicker. It defines the use cases where LED flicker occurs, and related consequences. It further defines a test methodology and metrics for evaluating an imaging systems susceptibility to LED flicker.