BLADE: Filter Learning for General Purpose Computational Photography

Reference

Pascal Getreuer, Ignacio Garcia-Dorado, John Isidoro, Sungjoon Choi, Frank Ong, Peyman Milanfar. “BLADE: Filter Learning for General Purpose Image Processing.” 2018 IEEE International Conference on Computational Photography (ICCP), pp. 1–11, 2018.

Article permalink: https://doi.org/10.1109/ICCPHOT.2018.8368476

@inproceedings{getreuer2018blade,
    title={{BLADE:} Filter learning for general purpose computational photography},
    author = {Pascal Getreuer and Ignacio Garcia-Dorado and John Isidoro and
              Sungjoon Choi and Frank Ong and Peyman Milanfar},
    booktitle={2018 IEEE International Conference on
               Computational Photography (ICCP)},
    pages={1-11},
    year={2018},
    organization={IEEE},
    doi={10.1109/ICCPHOT.2018.8368476},
}

Abstract

The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters. We describe a generalization of RAISR, which we name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable edge-adaptive filtering framework that is general, simple, computationally efficient, and useful for a wide range of problems in computational photography. We show applications to operations which may appear in a camera pipeline including denoising, demosaicing, and stylization.


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