ML Anthology

Avoiding the Deconvolution: Framework Oriented Color Transfer for Enhancing Low-Light Images

Laura Florea, Corneliu Florea, Ciprian Ionascu
CVPRW 2016 pp. 936-944
doi:10.1109/CVPRW.2016.121 /cvprw/2016/florea2016cvprw-avoiding/

Abstract

In this paper we introduce a novel color transfer method to address the underexposed image amplification problem. Targeted scenario implies a dual acquisition, containing a normally exposed, possibly blurred, image and an underexposed/low-light but sharp one. The problem of enhancing the low-light image is addressed as a color transfer problem. To properly solve the color transfer, the scene is split into perceptual frameworks and we propose a novel piece-wise approximation. The proposed method is shown to lead to robust results from both an objective and a subjective point of view.

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Cite

Text

Florea et al. "Avoiding the Deconvolution: Framework Oriented Color Transfer for Enhancing Low-Light Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2016. doi:10.1109/CVPRW.2016.121

Markdown

[Florea et al. "Avoiding the Deconvolution: Framework Oriented Color Transfer for Enhancing Low-Light Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2016.](https://mlanthology.org/cvprw/2016/florea2016cvprw-avoiding/) doi:10.1109/CVPRW.2016.121

BibTeX

@inproceedings{florea2016cvprw-avoiding,
  title     = {{Avoiding the Deconvolution: Framework Oriented Color Transfer for Enhancing Low-Light Images}},
  author    = {Florea, Laura and Florea, Corneliu and Ionascu, Ciprian},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2016},
  pages     = {936-944},
  doi       = {10.1109/CVPRW.2016.121},
  url       = {https://mlanthology.org/cvprw/2016/florea2016cvprw-avoiding/}
}