Event-based Asynchronous HDR Imaging by Temporal Incident Light Modulation

TL;DR

This work tackles dynamic-range limitations of traditional frame-based imaging by leveraging a pixel-asynchronous approach with a Dynamic Vision Sensor (DVS) and LCD-based irradiance modulation to trigger per-pixel events whose timestamps encode scene radiance. A temporal-weighted reconstruction algorithm converts event streams into HDR intensity images, with a c-map calibration to suppress fixed-pattern noise and pseudo-events. The proposed AsynHDR system achieves high dynamic range (e.g., $DR \approx 102.6$ dB in tests) and robust HDR performance in challenging indoor and outdoor scenes, without frame-based acquisition or active lighting for standard scenes. The authors discuss limitations in frame rate ($\approx 20$ fps) and motion handling, and outline future work toward color HDR with Bayer DVS and improved motion robustness.

Abstract

Dynamic Range (DR) is a pivotal characteristic of imaging systems. Current frame-based cameras struggle to achieve high dynamic range imaging due to the conflict between globally uniform exposure and spatially variant scene illumination. In this paper, we propose AsynHDR, a Pixel-Asynchronous HDR imaging system, based on key insights into the challenges in HDR imaging and the unique event-generating mechanism of Dynamic Vision Sensors (DVS). Our proposed AsynHDR system integrates the DVS with a set of LCD panels. The LCD panels modulate the irradiance incident upon the DVS by altering their transparency, thereby triggering the pixel-independent event streams. The HDR image is subsequently decoded from the event streams through our temporal-weighted algorithm. Experiments under standard test platform and several challenging scenes have verified the feasibility of the system in HDR imaging task.

Figures (6)

• Figure 1: (a) Optical schematic diagram of our AsynHDR system. (b) Event triggering demonstration. The point cloud diagram illustrates events triggered by the dynamic modulation of LCD panels, where red represents positive events and blue represents negative ones. (c) Physical demonstration of the system.
• Figure 2: Pixel-wise presentation of events triggered at different light intensities. (a) Sampling points chosen from continuous stepped radiance levels on a gray-scale test card. (b) The events triggered at different points along the timeline, where blue lines represent the positive events ($p_{i}$=+1), and colored triangles indicate different order events for each pixel.
• Figure 3: Dynamic range test and denoise algorithm experiment. (a) The stepped transmission brightness test card. (b) Illustration of the dynamic range test curve for the system. The table at the bottom displays the transmittance density of different filters for the filter array.
• Figure 4: Analysis of Algorithm SNR. (a) The SNR curves of different uniform radiance regions on the test card under various temporal weighting enhancement strategies with/without c-map adjustment. (b) The average step-radiance SNR under different k-factor exponential temporal weighting.
• Figure 5: Imaging results of event flow using different reconstruction methods.For each algorithm, we provide the zoom-in images of the orange block. (a) Directly accumulate events to image (raw integral + c-map adjust). (b) Imaging by exponentially temporal weighted summation of events (ours + c-map adjust). (c) The linear temporal weighted summation of events to image (linear + c-map adjust). (d) Imaging results of the frame camera with identical resolution and sensor size under the same optical system (GT).