Intensity-Robust Autofocus for Spike Camera

Changqing Su, Zhiyuan Ye, Yongsheng Xiao, You Zhou, Zhen Cheng, Bo Xiong, Zhaofei Yu, Tiejun Huang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 25018-25027

Spike cameras a novel neuromorphic visual sensor can capture full-time spatial information through spike stream offering ultra-high temporal resolution and an extensive dynamic range. Autofocus control (AC) plays a pivotal role in a camera to efficiently capture information in challenging real-world scenarios. Nevertheless due to disparities in data modality and information characteristics compared to frame stream and event stream the current lack of efficient AC methods has made it challenging for spike cameras to adapt to intricate real-world conditions. To address this challenge we introduce a spike-based autofocus framework that includes a spike-specific focus measure called spike dispersion (SD) which effectively mitigates the influence of variations in scene light intensity during the focusing process by leveraging the spike camera's ability to record full-time spatial light intensity. Additionally the framework integrates a fast search strategy called spike-based golden fast search (SGFS) allowing rapid focal positioning without the need for a complete focus range traversal. To validate the performance of our method we have collected a spike-based autofocus dataset (SAD) containing synthetic data and real-world data under varying scene brightness and motion scenarios. Experimental results on these datasets demonstrate that our method offers state-of-the-art accuracy and efficiency. Furthermore experiments with data captured under varying scene brightness levels illustrate the robustness of our method to changes in light intensity during the focusing process.