Pigmento: Pigment-Based Image Analysis and Editing

IEEE Transactions on Visualization and Computer Graphics (TVCG)

Publication date: August 1, 2018

Jianchao Tan, Stephen DiVerdi, Jingwan (Cynthia) Lu, Yotam Gingold

The colorful appearance of a physical painting is determined by the distribution of paint pigments across the canvas, which we model as a per-pixel mixture of a small number of pigments with multispectral absorption and scattering coefficients. We present an algorithm to efficiently recover this structure from an RGB image, yielding a plausible set of pigments and a low RGB reconstruction error. We show that under certain circumstances we are able to recover pigments that are close to ground truth, while in all cases our results are always plausible. Using our decomposition, we repose standard digital image editing operations as operations in pigment space rather than RGB, with interestingly novel results. We demonstrate tonal adjustments, selection masking, cut-copy-paste, recoloring, palette summarization, and edge enhancement.

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