Intrinsic Image Decomposition Using Multi‐Scale Measurements and Sparsity
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Date
2017
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© 2017 The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Automatic decomposition of intrinsic images, especially for complex real‐world images, is a challenging under‐constrained problem. Thus, we propose a new algorithm that generates and combines multi‐scale properties of chromaticity differences and intensity contrast. The key observation is that the estimation of image reflectance, which is neither a pixel‐based nor a region‐based property, can be improved by using multi‐scale measurements of image content. The new algorithm iteratively coarsens a graph reflecting the reflectance similarity between neighbouring pixels. Then multi‐scale reflectance properties are aggregated so that the graph reflects the reflectance property at different scales. This is followed by a sparse regularization on the whole reflectance image, which enforces the variation in reflectance images to be high‐frequency and sparse. We formulate this problem through energy minimization which can be solved efficiently within a few iterations. The effectiveness of the new algorithm is tested with the Massachusetts Institute of Technology (MIT) dataset, the Intrinsic Images in the Wild (IIW) dataset, and various natural images.Automatic decomposition of intrinsic images, especially for complex real‐world images, is a challenging under‐constrained problem. Thus, we propose a new algorithm that generates and combines multi‐scale properties of chromaticity differences and intensity contrast. The key observation is that the estimation of image reflectance, which is neither a pixel‐based nor a region‐based property, can be improved by using multi‐scale measurements of image content. The new algorithm iteratively coarsens a graph reflecting the reflectance similarity between neighbouring pixels.
Description
@article{10.1111:cgf.12874,
journal = {Computer Graphics Forum},
title = {{Intrinsic Image Decomposition Using Multi‐Scale Measurements and Sparsity}},
author = {Ding, Shouhong and Sheng, Bin and Hou, Xiaonan and Xie, Zhifeng and Ma, Lizhuang},
year = {2017},
publisher = {© 2017 The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.12874}
}