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|Title:||Color-aware regularization for gradient domain image manipulation|
|Source:||Deng, F.,Kim, S.J.,Tai, Y.-W.,Brown, M.S. (2013). Color-aware regularization for gradient domain image manipulation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7727 LNCS (PART 4) : 392-405. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-37447-0_30|
|Abstract:||We propose a color-aware regularization for use with gradient domain image manipulation to avoid color shift artifacts. Our work is motivated by the observation that colors of objects in natural images typically follow distinct distributions in the color space. Conventional regularization methods ignore these distributions which can lead to undesirable colors appearing in the final output. Our approach uses an anisotropic Mahalanobis distance to control output colors to better fit original distributions. Our color-aware regularization is simple, easy to implement, and does not introduce significant computational overhead. To demonstrate the effectiveness of our method, we show the results with and without our color-aware regularization on three gradient domain tasks: gradient transfer, gradient boosting, and saliency sharpening. © 2013 Springer-Verlag.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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