A Study of Two CNN Demosaicking Algorithms
Thibaud Ehret, Gabriele Facciolo
→ BibTeX
@article{ipol.2019.274,
    title   = {{A Study of Two CNN Demosaicking Algorithms}},
    author  = {Ehret, Thibaud and Facciolo, Gabriele},
    journal = {{Image Processing On Line}},
    volume  = {9},
    pages   = {220--230},
    year    = {2019},
    doi     = {10.5201/ipol.2019.274},
}
% if your bibliography style doesn't support doi fields:
    note    = {\url{https://doi.org/10.5201/ipol.2019.274}}
published
2019-09-05
reference
Thibaud Ehret, and Gabriele Facciolo, A Study of Two CNN Demosaicking Algorithms, Image Processing On Line, 9 (2019), pp. 220–230. https://doi.org/10.5201/ipol.2019.274

Communicated by Miguel Colom and Jean-Michel Morel
Demo edited by Thibaud Ehret

Abstract

Most cameras capture the information of only one color for a given pixel. This results in a mosaicked image that must be interpolated to get three colors at each pixel. The step going from a mosaicked image to a regular RGB image is called demosaicking. This paper studies two recent demosaicking methods based on convolutional neural networks that achieve artifact-free state-of-the-art results: Deep joint demosaicking and denoising by Gharbi et al. and Color image demosaicking via deep residual learning by Tan et al. We show that these methods beat by almost two decibels the best human-crafted methods, while being faster by one order of magnitude. This, arguably, seals the destiny of human-crafted methods on this subject.

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