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Intrinsic Image Decomposition Using a Sparse Representation of Reflectance
Dec. 2013 (vol. 35 no. 12)
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
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
Li Shen, Chuohao Yeo, Binh-Son Hua, "Intrinsic Image Decomposition Using a Sparse Representation of Reflectance," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 12, pp. 2904-2915, Dec. 2013, doi:10.1109/TPAMI.2013.136
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