同时优化事件传感器的对齐与去噪，提升运动估计精度
Joint Alignment and Denoising for Event-Based Vision Sensors Using Regret-based Pareto Optimization
- 将对齐与去噪联合建模为矛盾目标的帕累托优化
- 利用对比图方差最大化对齐、最小化去噪，提升效果
- 适合需要高精度事件数据的视觉系统开发者
本文提出一种面向事件相机（EVS）的联合对齐与去噪方法。现有方法通常将事件对齐（EA）与事件去噪（ED）作为独立模块处理，但这种分离导致困境：无去噪时对齐受噪声干扰，无对齐时去噪难以区分信号与噪声事件。为此，本文基于每个像素的事件计数构建对比图，将对齐建模为最大化其方差，去噪建模为最小化方差，形成双目标帕累托优化问题，并采用后悔策略求解。在去噪和运动估计任务上的实验表明，该方法优于现有方法。
原文摘要 · Abstract (English)
This paper proposes a joint alignment and denoising method for event-based vision sensors (EVSs). Existing signal processing methods for EVSs typically perform event alignment (EA) and event denoising (ED) as separate modules. However, this separation creates a dilemma: without ED, EA is biased by noise, whereas without EA, ED struggles to distinguish signal events from noise ones. To address this dilemma, we jointly optimize EA and ED by formulating a bi-objective Pareto optimization problem. Our formulation is built upon a contrast map that counts the number of events localized in each pixel. With a contrast map, we can formulate EA as maximizing its variance and ED as minimizing the variance. We cast these two conflicting problems as a Pareto optimization and use a regret strategy to obtain a solution. Experimental results on denoising and motion estimation demonstrate that our method achieves improvements against alternative ones.
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