Neural Image Representations for Multi-Image Fusion and Layer Separation
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We propose a framework for aligning and fusing multiple images into a single coordinate-based neural representations. Our framework targets burst images that have misalignment due to camera ego motion and small changes in the scene. We describe different strategies for alignment depending on the assumption of the scene motion, namely, perspective planar (i.e., homography), optical flow with minimal scene change, and optical flow with notable occlusion and disocclusion. Our framework effectively combines the multiple inputs into a single neural implicit function without the need for selecting one of the images as a reference frame. We demonstrate how to use this multi-frame fusion framework for various layer separation tasks.
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