2017/07/26 by Jianchao Tan, Tan, Jianchao, Stephen DiVerdi +5 · 2 citations
Computer Science · Physics and Astronomy · #Advanced Vision and Imaging #Color Science and Applications #FOS: Computer and information sciences #Graphics (cs.GR) #Image Enhancement Techniques #cs.GR
paper · pdf · doi:10.48550/arxiv.1707.08323
add copyright to images; add acknowledgements, is accepted by IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG)
openalex publication_date 2017/07/26 · arxiv created 2018/07/19 · arxiv updated 2018/07/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.