基于过完备像素对比学习的运动模糊图像对齐
Aligning Motion-Blurred Images Using Contrastive Learning on Overcomplete Pixels
AI总结：
该研究提出一种基于对比学习的过完备像素级特征学习方法，可实现运动模糊等不变性，能用于真实场景下运动模糊视频的帧对齐，且过完备像素可编码物体身份与相对坐标。
摘要：
我们提出了一种新的对比目标，用于学习对运动模糊具有不变性的过完备像素级特征。在自监督训练过程中，通过对无标注图像施加对应的变换，还可以学习其他不变性（如姿态、光照或天气）。我们证明，用该目标训练的简单U-Net能够生成可用于对齐未见过视频帧的局部特征，该视频由移动相机在真实且具有挑战性的条件下拍摄。通过精心设计的简单示例，我们还表明过完备像素可以编码图像中物体的身份以及像素相对于这些物体的坐标。
英文摘要：
We propose a new contrastive objective for learning overcomplete pixel-level features that are invariant to motion blur. Other invariances (e.g., pose, illumination, or weather) can be learned by applying the corresponding transformations on unlabeled images during self-supervised training. We showcase that a simple U-Net trained with our objective can produce local features useful for aligning the frames of an unseen video captured with a moving camera under realistic and challenging conditions. Using a carefully designed toy example, we also show that the overcomplete pixels can encode the identity of objects in an image and the pixel coordinates relative to these objects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
