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and centroids are calculated. The lower bound is used to filter non-nearest centroids for the purpose of reducing computational costs. ERVQ is noticeably optimized in terms of time efficiency on quantizing vectors when combining with this method. To evaluate the accuracy that vectors are approximated by their quantization outputs, an ERVQbased exhaustive method for approximate nearest neighbor search is implemented. Experimental results on three datasets demonstrate that our approaches outperform the stateof-the-art methods over vector quantization and approximate nearest neighbor search.

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We propose an effective progressive optimization algorithm to learn the decomposition process. Our approach can also be extended to portrait tattoo removal and watermark removal. Qualitative and quantitative experiments on a real-world portrait shadow dataset demonstrate that our approach achieves comparable performance with supervised shadow removal methods. Our source code is available at this repository. CCS CONCEPTS• Computing methodologies → Computational photography; Image processing.