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Intrinsic Image Decomposition Using Structure-Texture Separation and Surface Normals*
Junho Jeon1, Sunghyun Cho2, Xin Tong3, and Seungyong Lee1
1POSTECH, Korea
2Adobe Research, Korea
3Microsoft Research Asia, China
Abstract. While intrinsic image decomposition has been studied extensively during the past a few decades, it is still a challenging problem. This is partly because commonly used constraints on shading and reflectance are often too restrictive to capture an important property of natural images, i.e., rich textures. In this paper, we propose a novel image model for handling textures in intrinsic image decomposition, which enables us to produce high quality results even with simple constraints. We also propose a novel constraint based on surface normals obtained from an RGB-D image. Assuming Lambertian surfaces, we formulate the constraint based on a locally linear embedding framework to promote local and global consistency on the shading layer. We demonstrate that combining the novel texture-aware image model and the novel surface normal based constraint can produce superior results to existing approaches.
Keywords: intrinsic image decomposition, structure-texture separation, RGB-D image
Electronic Supplementary Material:
LNCS 8695, p. 218 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014