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Intrinsic Image Decomposition Using Multi-Scale Measurements and Sparsity.
- Source :
- Computer Graphics Forum; Sep2017, Vol. 36 Issue 6, p251-261, 11p
- Publication Year :
- 2017
-
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 L<subscript>0</subscript> 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- CHROMATICITY
ALGORITHMS
REFLECTANCE
OPTICAL properties
Subjects
Details
- Language :
- English
- ISSN :
- 01677055
- Volume :
- 36
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- Computer Graphics Forum
- Publication Type :
- Academic Journal
- Accession number :
- 124865747
- Full Text :
- https://doi.org/10.1111/cgf.12874