Pigmento: Pigment-Based Image Analysis and Editing
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 str...
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| Veröffentlicht in: | IEEE transactions on visualization and computer graphics 2019-09, Vol.25 (9), p.2791-2803 |
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| container_title | IEEE transactions on visualization and computer graphics |
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| creator | Tan, Jianchao DiVerdi, Stephen Lu, Jingwan Gingold, Yotam |
| description | 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. |
| doi_str_mv | 10.1109/TVCG.2018.2858238 |
| format | Article |
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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. 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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.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>30040646</pmid><doi>10.1109/TVCG.2018.2858238</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-9862-2654</orcidid><orcidid>https://orcid.org/0000-0002-5381-2104</orcidid><orcidid>https://orcid.org/0000-0002-6694-3381</orcidid></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | Absorption Algorithms color Computational modeling Digital imaging Editing Ground truth Image analysis Image color analysis kubelka-munk layering Masking Mathematical model mixing non-photorealistic editing NPR paint pigment Pigments RGB Scattering Scattering coefficients |
| title | Pigmento: Pigment-Based Image Analysis and Editing |
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