NDLI: Non-rigid dense correspondence with applications for image enhancement

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Non-rigid dense correspondence with applications for image enhancement

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Non-rigid dense correspondence with applications for image enhancement

Content Provider	ACM Digital Library
Author	Shechtman, Eli HaCohen, Yoav Goldman, Dan B. Lischinski, Dani
Abstract	This paper presents a new efficient method for recovering reliable local sets of dense correspondences between two images with some shared content. Our method is designed for pairs of images depicting similar regions acquired by different cameras and lenses, under non-rigid transformations, under different lighting, and over different backgrounds. We utilize a new coarse-to-fine scheme in which nearest-neighbor field computations using Generalized PatchMatch [Barnes et al. 2010] are interleaved with fitting a global non-linear parametric color model and aggregating consistent matching regions using locally adaptive constraints. Compared to previous correspondence approaches, our method combines the best of two worlds: It is dense, like optical flow and stereo reconstruction methods, and it is also robust to geometric and photometric variations, like sparse feature matching. We demonstrate the usefulness of our method using three applications for automatic example-based photograph enhancement: adjusting the tonal characteristics of a source image to match a reference, transferring a known mask to a new image, and kernel estimation for image deblurring.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF MP4
ISBN	9781450309431
DOI	10.1145/1964921.1964965
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-08-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Deblurring Patchmatch Nearest neighbor field Color transfer Correspondence
Content Type	Audio Text
Resource Type	Article

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