Aligning Motion-Blurred Images Using Contrastive Learning on Overcomplete Pixels

Leonid Pogorelyuk; Stefan T. Radev

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arXiv · 2410.07410

Aligning Motion-Blurred Images Using Contrastive Learning on Overcomplete Pixels

Leonid Pogorelyuk·Stefan T. Radev

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Abstract

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.

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BibTeX RIS

Leonid Pogorelyuk, Stefan T. Radev. 2024-10-09. Aligning Motion-Blurred Images Using Contrastive Learning on Overcomplete Pixels. https://arxiv.org/abs/2410.07410

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