Issue No. 12 - Dec. (2013 vol. 35)
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.
Image color analysis, Wavelet transforms, Image decomposition, Multiresolution analysis, Reflectance, Image edge detection
Li Shen, Chuohao Yeo and Binh-Son Hua, "Intrinsic Image Decomposition Using a Sparse Representation of Reflectance," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. 12, pp. 2904-2915, 2013.