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（CMax）技术广泛应用于基于事件的视觉系统中，用于估计相机的运动参数并生成高对比度图像。然而，这些技术对噪声敏感，且当场景中包含的结构事件少于噪声事件时，会面临多极值问题——即对比度在多个位置同时升高。这使得相机运动估计任务极具挑战性，尤其对神经形态地球观测造成困难：若无法准确估计运动参数，便无法生成高对比度地图，导致重要细节丢失。现有基于CMax的类似方法通过修改或增强目标函数来解决该问题，使其能够收敛至正确的运动参数。我们提出的解决方案通过在校正对比度之前修正扭曲事件，克服了多极值问题和噪声敏感性缺陷，并具有以下优势：不依赖事件数据本身、无需先验相机运动信息、且保持CMax流水线其余部分不变。这确保了对比度仅在正确运动参数周围保持较高水平。利用国际空间站（ISS）的新型数据集，我们的方法通过分析补偿技术能够生成更优的运动补偿地图。代码已开源在：\url{https://github.com/neuromorphicsystems/event_warping}