Traditional cameras measure image intensity. Event cameras, by contrast, measure per-pixel temporal intensity changes asynchronously. Recovering intensity from events is a popular research topic since the reconstructed images inherit the high dynamic range (HDR) and high-speed properties of events; hence they can be used in many robotic vision applications and to generate slow-motion HDR videos. However, state-of-the-art methods tackle this problem by training an event-to-image recurrent neural network (RNN), which lacks explainability and is difficult to tune. In this work we show, for the first time, how tackling the joint problem of motion and intensity estimation leads us to model event-based image reconstruction as a linear inverse problem that can be solved without training an image reconstruction RNN. Instead, classical and learning-based image priors can be used to solve the problem and remove artifacts from the reconstructed images. The experiments show that the proposed approach generates images with visual quality on par with state-of-the-art methods despite only using data from a short time interval (i.e., without recurrent connections). Our method can also be used to improve the quality of images reconstructed by approaches that first estimate the image Laplacian; here our method can be interpreted as Poisson reconstruction guided by image priors.
翻译:传统相机测量图像强度。 对比之下, 事件相机, 以不同步的方式测量每像素时间强度变化。 从事件中恢复强度是一个受欢迎的研究课题, 因为重建后的图像继承了高动态范围( HDR) 和事件的高速特性; 因此它们可以用于许多机器人视觉应用, 并生成慢动的图像。 但是, 最先进的方法通过培训一个事件到图像的经常性神经网络( RNNN)来解决这个问题, 它缺乏解释性且难以调和。 在这项工作中, 我们第一次展示了如何解决运动和强度估计的共同问题, 导致我们将事件图像重建作为线性反问题进行模型, 不训练图像重建 RNNN 就可以解决。 相反, 古典和基于学习的图像前期可以用来解决问题, 从重建图像中移除艺术品。 实验显示, 拟议的方法能够生成具有视觉质量的图像, 与状态- 方法相同, 尽管只能使用很短的时间间隔的数据( i., 没有经常性连接 ) 。 我们的方法也可以用来将这里的图像重建方法加以改进, 。