Deep Joint Design of Color Filter Arrays and Demosaicing
| dc.contributor.author | Henz, Bernardo | en_US |
| dc.contributor.author | Gastal, Eduardo S. L. | en_US |
| dc.contributor.author | Oliveira, Manuel M. | en_US |
| dc.contributor.editor | Gutierrez, Diego and Sheffer, Alla | en_US |
| dc.date.accessioned | 2018-04-14T18:24:58Z | |
| dc.date.available | 2018-04-14T18:24:58Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | We present a convolutional neural network architecture for performing joint design of color filter array (CFA) patterns and demosaicing. Our generic model allows the training of CFAs of arbitrary sizes, optimizing each color filter over the entire RGB color space. The patterns and algorithms produced by our method provide high-quality color reconstructions. We demonstrate the effectiveness of our approach by showing that its results achieve higher PSNR than the ones obtained with state-of-the-art techniques on all standard demosaicing datasets, both for noise-free and noisy scenarios. Our method can also be used to obtain demosaicing strategies for pre-defined CFAs, such as the Bayer pattern, for which our results also surpass even the demosaicing algorithms specifically designed for such a pattern. | en_US |
| dc.description.number | 2 | |
| dc.description.sectionheaders | Image Magic | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 37 | |
| dc.identifier.doi | 10.1111/cgf.13370 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 389-399 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.13370 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13370 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | Computing methodologies | |
| dc.subject | Computational photography | |
| dc.subject | Neural networks | |
| dc.title | Deep Joint Design of Color Filter Arrays and Demosaicing | en_US |