Contrast maximization (CMax) techniques are widely used in event-based vision systems to estimate the motion parameters of the camera and generate high-contrast images. However, these techniques are noise-intolerance and suffer from the multiple extrema problem which arises when the scene contains more noisy events than structure, causing the contrast to be higher at multiple locations. This makes the task of estimating the camera motion extremely challenging, which is a problem for neuromorphic earth observation, because, without a proper estimation of the motion parameters, it is not possible to generate a map with high contrast, causing important details to be lost. Similar methods that use CMax addressed this problem by changing or augmenting the objective function to enable it to converge to the correct motion parameters. Our proposed solution overcomes the multiple extrema and noise-intolerance problems by correcting the warped event before calculating the contrast and offers the following advantages: it does not depend on the event data, it does not require a prior about the camera motion, and keeps the rest of the CMax pipeline unchanged. This is to ensure that the contrast is only high around the correct motion parameters. Our approach enables the creation of better motion-compensated maps through an analytical compensation technique using a novel dataset from the International Space Station (ISS). Code is available at \url{https://github.com/neuromorphicsystems/event_warping}

翻译：对比度最大化技术广泛应用于基于事件的视觉系统中，用于估计相机运动参数并生成高对比度图像。然而，这类技术对噪声敏感且存在多极值问题——当场景中噪声事件多于结构事件时，对比度会在多个位置异常升高。这导致相机运动参数估计极具挑战性，对神经形态地球观测尤为不利：若无法准确估计运动参数，便无法生成高对比度地图，造成重要细节丢失。现有基于对比度最大化的方法通过修改或增强目标函数来促使模型收敛到正确运动参数。我们提出的解决方案在计算对比度前对扭曲事件进行校正，从而克服多极值与噪声敏感问题，具有以下优势：不依赖事件数据、无需相机运动先验、且保持对比度最大化流程其余部分不变，确保仅当运动参数正确时对比度才显著升高。该方法利用国际空间站新型数据集，通过解析补偿技术生成更优的运动补偿地图。代码开源于 \url{https://github.com/neuromorphicsystems/event_warping}