Content-Preserving Tone Adjustment for Image Enhancement

Simone Bianco, Claudio Cusano, Flavio Piccoli, Raimondo Schettini

CVPRW 2019 pp. 1936-1943

doi:10.1109/CVPRW.2019.00245 /cvprw/2019/bianco2019cvprw-contentpreserving/

Abstract

We propose a novel method based on Convolutional Neural Networks for content-preserving tone adjustment. The method is at the same time fast and accurate since we decouple the inference of the parameters and the color transform: the parameters are inferred from a downsampled version of the input image and the transformation is applied to the full resolution input. The method includes two steps of image enhancement: the first one is a global color transformation, while the second one is a local transformation. Experiments conducted on the DPED - DSLR Photo Enhancement Dataset, that has been used for the NTIRE19 Image Enhancement Challenge, and on the MIT-Adobe FiveK dataset, that is widely used for image enhancement, demonstrate the effectiveness of the proposed method.

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Cite

Text

Bianco et al. "Content-Preserving Tone Adjustment for Image Enhancement." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019. doi:10.1109/CVPRW.2019.00245

Markdown

[Bianco et al. "Content-Preserving Tone Adjustment for Image Enhancement." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/bianco2019cvprw-contentpreserving/) doi:10.1109/CVPRW.2019.00245

BibTeX

@inproceedings{bianco2019cvprw-contentpreserving,
  title     = {{Content-Preserving Tone Adjustment for Image Enhancement}},
  author    = {Bianco, Simone and Cusano, Claudio and Piccoli, Flavio and Schettini, Raimondo},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2019},
  pages     = {1936-1943},
  doi       = {10.1109/CVPRW.2019.00245},
  url       = {https://mlanthology.org/cvprw/2019/bianco2019cvprw-contentpreserving/}
}