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.