Dehazing using Non-local Regularization with Iso-depth Neighbor-Fields

Incheol Kim, Min H. Kim

2017

Abstract

Removing haze from a single image is a severely ill-posed problem due to the lack of the scene information. General dehazing algorithms estimate airlight initially using natural image statistics and then propagate the incompletely estimated airlight to build a dense transmission map, yielding a haze-free image. Propagating haze is different from other regularization problems, as haze is strongly correlated with depth according to the physics of light transport in participating media. However, since there is no depth information available in single-image dehazing, traditional regularization methods with a common grid random field often suffer from haze isolation artifacts caused by abrupt changes in scene depths. In this paper, to overcome the haze isolation problem, we propose a non-local regularization method by combining Markov random fields (MRFs) with nearest-neighbor fields (NNFs), based on our insightful observation that the NNFs searched in a hazy image associate patches at the similar depth, as local haze in the atmosphere is proportional to its depth. We validate that the proposed method can regularize haze effectively to restore a variety of natural landscape images, as demonstrated in the results. This proposed regularization method can be used separately with any other dehazing algorithms to enhance haze regularization.

Paper Citation

in Harvard Style

Kim I. and H. Kim M. (2017). Dehazing using Non-local Regularization with Iso-depth Neighbor-Fields . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 77-88. DOI: 10.5220/0006132400770088

in Bibtex Style

@conference{visapp17,
author={Incheol Kim and Min H. Kim},
title={Dehazing using Non-local Regularization with Iso-depth Neighbor-Fields},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={77-88},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006132400770088},
isbn={978-989-758-225-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Dehazing using Non-local Regularization with Iso-depth Neighbor-Fields
SN - 978-989-758-225-7
AU - Kim I.
AU - H. Kim M.
PY - 2017
SP - 77
EP - 88
DO - 10.5220/0006132400770088