Deriving Intrinsic Images from Image Sequences

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Deriving Intrinsic Images from Image Sequences (2001)

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by Yair Weiss

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BibTeX

@INPROCEEDINGS{Weiss01derivingintrinsic,
    author = {Yair Weiss},
    title = {Deriving Intrinsic Images from Image Sequences},
    booktitle = {},
    year = {2001},
    pages = {68--75}
}

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Abstract

Intrinsic images are a useful midlevel description of scenes proposed by Barrow and Tenebaum [1]. An image is decomposed into two images: a reflectance image and an illumination image. Finding such a decomposition remains a difficult problem in computer vision. Here we focus on a slightly easier problem: given a sequence of T images where the reflectance is constant and the illumination changes, can we recover T illumination images and a single reflectance image? We show that this problem is still ill-posed and suggest approaching it as a maximum-likelihood estimation problem. Following recent work on the statistics of natural images, we use a prior that assumes that illumination images will give rise to sparse filter outputs. We show that this leads to a simple, novel algorithm for recovering reflectance images. We illustrate the algorithm's performance on real and synthetic image sequences.

Keyphrases

intrinsic image illumination image image sequence reflectance image synthetic image sequence maximum-likelihood estimation problem single reflectance image natural image useful midlevel description computer vision difficult problem filter output illumination change novel algorithm recent work

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