Separating reflections from a single image using local features

Anat Levin*, Assaf Zomet, Yair Weiss

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

127 Scopus citations

Abstract

When we take a picture through a window the image we obtain is often a linear superposition of two images: the image of the scene beyond the window plus the image of the scene reflected by the window. Decomposing the single input image into two images is a massively ill-posed problem: in the absence of additional knowledge about the scene being viewed there is an infinite number of valid decompositions. In this paper we describe an algorithm that uses an extremely simple form of prior knowledge to perform the decomposition. Given a single image as input, the algorithm searches for a decomposition into two images that minimize the total amount of edges and corners. The search is performed using belief propagation on a patch representation of the image. We show that this simple prior is surprisingly powerful: our algorithm obtains "correct" separations on challenging reflection scenes using only a single image.

Original languageAmerican English
Pages (from-to)I306-I313
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume1
StatePublished - 2004
EventProceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004 - Washington, DC, United States
Duration: 27 Jun 20042 Jul 2004

Fingerprint

Dive into the research topics of 'Separating reflections from a single image using local features'. Together they form a unique fingerprint.

Cite this