Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes

CVPR 2017

doi:10.1109/CVPR.2017.71 /cvpr/2017/golestaneh2017cvpr-spatiallyvarying/

Abstract

The detection of spatially-varying blur without having any information about the blur type is a challenging task. In this paper, we propose a novel effective approach to address this blur detection problem from a single image without requiring any knowledge about the blur type, level, or camera settings. Our approach computes blur detection maps based on a novel High-frequency multiscale Fusion and Sort Transform (HiFST) of gradient magnitudes. The evaluations of the proposed approach on a diverse set of blurry images with different blur types, levels, and contents demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods qualitatively and quantitatively.

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Text

Golestaneh and Karam. "Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes." Conference on Computer Vision and Pattern Recognition, 2017. doi:10.1109/CVPR.2017.71

Markdown

[Golestaneh and Karam. "Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes." Conference on Computer Vision and Pattern Recognition, 2017.](https://mlanthology.org/cvpr/2017/golestaneh2017cvpr-spatiallyvarying/) doi:10.1109/CVPR.2017.71

BibTeX

@inproceedings{golestaneh2017cvpr-spatiallyvarying,
  title     = {{Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes}},
  author    = {Golestaneh, S. Alireza and Karam, Lina J.},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2017},
  doi       = {10.1109/CVPR.2017.71},
  url       = {https://mlanthology.org/cvpr/2017/golestaneh2017cvpr-spatiallyvarying/}
}