Event cameras are a novel type of visual sensor inspired by the biological retina， capable of asynchronously capturing pixel brightness changes with microsecond-level temporal resolution. Unlike traditional cameras that capture frames at fixed intervals， event cameras only trigger an event when pixel brightness exceeds a preset threshold. This unique mechanism endows event cameras with an ultra-wide dynamic range of up to 140 decibels and exceptional temporal resolution， granting them significant advantages in high-speed， high-dynamic-range scenarios such as object tracking， motion detection， and Simultaneous Localization and Mapping （SLAM）. These capabilities enable event cameras to capture rapid movements and subtle changes in dynamic scenes that conventional cameras might miss. However， event cameras have a significant limitation： they can not directly capture grayscale images in static scenes. This is because when a scene is stationary and light intensity remains constant， the camera detects no change in light intensity， failing to meet the conditions for event triggering. These characteristics restrict their application in scenarios requiring complete brightness information， such as static object or scene recognition and measurement in traditional image processing tasks. Consequently， despite their ultra-high dynamic range， temporal resolution， and exceptional performance in dynamic environments， event cameras still face challenges in applications demanding detailed， continuous brightness data acquisition under static conditions.

To address the challenge of capturing static scenes with event cameras， this paper introduces a novel approach using mechanical shutter mapping for imaging. The core innovation lies in modulating brightness variations in static scenes through the rotation of a mechanical shutter， triggering events even in the absence of light intensity changes. By adjusting the shutter's rotation speed at varying rates， discrete events are generated， which are then reconstructed into High Dynamic Range （HDR） grayscale images. This technique overcomes a significant limitation of traditional event cameras， which struggle to capture static scenes due to the lack of natural light variations. A key aspect of this method is the calibration process， which ensures that the generated event data is accurately aligned with the actual light intensity of the scene， enabling precise and accurate grayscale image reconstruction. In this setup， the mechanical shutter acts as an active driving mechanism， inducing event generation by modulating the scene's brightness. This allows the event camera to capture dynamic information in static scenes， where traditional event cameras would otherwise fail to register changes. As a result， this method significantly enhances the imaging capabilities of event cameras， broadening their potential applications in static environments with minimal or no natural light variations.

Experimental results show that the reconstructed HDR images achieve a dynamic range of up to 112 dB. This range allows the event camera to capture fine details in both dark and bright areas of the scene， overcoming the limitations of conventional cameras with fixed dynamic ranges. The calibration process further improves the accuracy of grayscale image reconstruction. Our experimental results demonstrate that shutter-based techniques successfully reconstruct grayscale images of static scenes under extreme lighting conditions. Compared to prior methods， this approach significantly improves image quality， reduces grayscale distortion， and preserves details often lost in conventional event reconstruction techniques. By generating events through controlled brightness variations， this method enhances the imaging capabilities of event cameras in static environments. Furthermore， the mechanical shutter mechanism facilitates portability， enabling adaptation to multiple event camera models and enhancing the system's versatility and practical applicability. Beyond expanding event cameras' capabilities in static scenes， this approach also provides a solution for event cameras operating under complex lighting conditions. Our shutter mapping technology offers a controlled event triggering mechanism， enabling event cameras to function in scenarios where traditional methods fail. This opensnew possibilities for event cameras in fields requiring high-quality grayscale images， such as industrial inspection.

This paper proposes and validates a shutter-mapped event camera method for grayscale imaging in high-dynamic-range static scenes. The core innovation of this approach lies in introducing a mechanical shutter as an active driving mechanism， which effectively overcomes the limitation that event cameras cannot generate event streams in static scenes due to the absence of brightness changes. By modulating the scene's brightness using controlled shutter rotations， the event camera can trigger events even in stationary conditions. Experimental results demonstrate that this method successfully reconstructs grayscale images of high-dynamic-range static scenes， achieving a dynamic range of up to 112 dB under extreme illumination conditions， all using a frameless event camera. Compared to existing methods， our approach significantly improves reconstruction quality， reduces grayscale distortion， and preserves fine details. Additionally， the proposed mechanical shutter triggering mechanism offers versatility and portability， providing a controllable active refresh mechanism that is compatible with various event camera models. In summary， the shutter mapping method extends the capabilities of event cameras in static scenes， enabling high-quality HDR grayscale image reconstruction. By inducing brightness variations through mechanical shutter actuation， event cameras can generate event streams and reconstruct HDR images， even in static scenes with extreme lighting conditions.