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Description
This paper considers the generic problem of dense alignment between two images, whether they be two frames of a video, two widely Different views of a scene, two paintings depicting similar content, etc. Whereas each such task is typically addressed with a domain-specific the solution, Near-duplicates interpolation or alignment, is an interesting new application, but large motion challenges existing methods. To address this issue, we adopt a feature extractor that shares weights across the scales and optimize our network with the Gram matrix loss that measures the correlation difference between features. Then the fine alignment is learned in an unsupervised manner by a deep network that optimizes a standard structural similarity metric (SSIM) between the two images. The results on BigBrain images show the performance of the proposed approach.
