CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.12 - Dec.

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Issue No.12 - Dec. (2013 vol.35)

pp: 2904-2915

Li Shen , Inst. for Infocomm Res., Singapore, Singapore

Chuohao Yeo , Inst. for Infocomm Res., Singapore, Singapore

Binh-Son Hua , Dept. of Comput. Sci., Nat. Univ. of Singapore, Singapore, Singapore

ABSTRACT

Intrinsic image decomposition is an important problem that targets the recovery of shading and reflectance components from a single image. While this is an ill-posed problem on its own, we propose a novel approach for intrinsic image decomposition using reflectance sparsity priors that we have developed. Our sparse representation of reflectance is based on a simple observation: Neighboring pixels with similar chromaticities usually have the same reflectance. We formalize and apply this sparsity constraint on local reflectance to construct a data-driven second-generation wavelet representation. We show that the reflectance component of natural images is sparse in this representation. We further propose and formulate a global sparse constraint on reflectance colors using the assumption that each natural image uses a small set of material colors. Using this sparse reflectance representation and the global constraint on a sparse set of reflectance colors, we formulate a constrained $(l_1)$-norm minimization problem for intrinsic image decomposition that can be solved efficiently. Our algorithm can successfully extract intrinsic images from a single image without using color models or any user interaction. Experimental results on a variety of images demonstrate the effectiveness of the proposed technique.

INDEX TERMS

Image color analysis, Wavelet transforms, Image decomposition, Multiresolution analysis, Reflectance, Image edge detection,multiresolution analysis, Intrinsic image decomposition, sparse reconstruction

CITATION

Li Shen, Chuohao Yeo, Binh-Son Hua, "Intrinsic Image Decomposition Using a Sparse Representation of Reflectance",

*IEEE Transactions on Pattern Analysis & Machine Intelligence*, vol.35, no. 12, pp. 2904-2915, Dec. 2013, doi:10.1109/TPAMI.2013.136REFERENCES