Bringing Rolling Shutter Images Alive with Dual Reversed Distortion
Bringing Rolling Shutter Images Alive with Dual Reversed Distortion
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DOI：
10.48550/arxiv.2203.06451
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Zhihang Zhong;Ming Cao;Xiao Sun;Zhirong Wu;Zhongyi Zhou;Yinqiang Zheng;Stephen Lin;Imari Sato
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文献类型：
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作者：
Zhihang Zhong;Ming Cao;Xiao Sun;Zhirong Wu;Zhongyi Zhou;Yinqiang Zheng;Stephen Lin;Imari Sato
滚动快门（RS）失真可以被解释为在RS相机曝光期间随时间从即时全局快门（GS）帧拾取一行像素的结果。这意味着每个瞬时GS帧的信息部分地但顺序地嵌入到行相关失真中。受这一事实的启发，我们解决了逆转这一过程的挑战性任务，即，从遭受RS失真的图像中提取未失真的GS帧。然而，由于RS失真与其他因素，如读出设置和相对速度的场景元素的相机，模型，只利用时间上相邻的图像之间的几何相关性遭受较差的通用性，在处理数据与不同的读出设置和动态场景与相机运动和对象运动。在本文中，而不是两个连续的帧，我们建议利用双RS相机拍摄的图像与反向RS方向，这个极具挑战性的任务。基于双反向畸变的对称性和互补性，我们提出了一种新的端到端模型IFED，通过迭代学习RS时间内的速度场来产生双光流序列。大量的实验结果表明，IFED是上级朴素级联方案，以及国家的最先进的，利用相邻的RS图像。最重要的是，虽然它是在合成数据集上训练的，但IFED在从真实世界的RS失真动态场景图像中检索GS帧序列方面是有效的。代码可在https://github.com/zzh-tech/Dual-Reversed-RS上获得。
Rolling shutter (RS) distortion can be interpreted as the result of picking a row of pixels from instant global shutter (GS) frames over time during the exposure of the RS camera. This means that the information of each instant GS frame is partially, yet sequentially, embedded into the row-dependent distortion. Inspired by this fact, we address the challenging task of reversing this process, i.e., extracting undistorted GS frames from images suffering from RS distortion. However, since RS distortion is coupled with other factors such as readout settings and the relative velocity of scene elements to the camera, models that only exploit the geometric correlation between temporally adjacent images suffer from poor generality in processing data with different readout settings and dynamic scenes with both camera motion and object motion. In this paper, instead of two consecutive frames, we propose to exploit a pair of images captured by dual RS cameras with reversed RS directions for this highly challenging task. Grounded on the symmetric and complementary nature of dual reversed distortion, we develop a novel end-to-end model, IFED, to generate dual optical flow sequence through iterative learning of the velocity field during the RS time. Extensive experimental results demonstrate that IFED is superior to naive cascade schemes, as well as the state-of-the-art which utilizes adjacent RS images. Most importantly, although it is trained on a synthetic dataset, IFED is shown to be effective at retrieving GS frame sequences from real-world RS distorted images of dynamic scenes. Code is available at https://github.com/zzh-tech/Dual-Reversed-RS.