Foggy Day Image Sharpening Algorithm Based on Depth of Field Estimation

DOI

10.2991/acaai-18.2018.16How to use a DOI?

Keywords

Image dehazing; depth of field estimation; White balancing; feature extraction

Abstract

Traditional dehazing methods always use the dark channel a priori method to recover the original image, which leads to the calculation of the transmission is large and takes too much time. Therefore, in this paper, we present a fog-day-based image sharpening algorithm based on depth of field estimation. This approach makes use of white balancing, which eliminates the color cast that is caused by the atmospheric color. Then we extract the depth features of the haze image and construct the depth of field estimation model. Finally, the original image is restored by estimating the depth of field of each pixel of the image. The experimental results show that the method we developed has the capability to remove the haze efficiently, by which the finest details and edges are enhanced significantly.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Changli Li
AU  - Qian Jia
PY  - 2018/03
DA  - 2018/03
TI  - Foggy Day Image Sharpening Algorithm Based on Depth of Field Estimation
BT  - Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)
PB  - Atlantis Press
SP  - 64
EP  - 68
SN  - 1951-6851
UR  - https://doi.org/10.2991/acaai-18.2018.16
DO  - 10.2991/acaai-18.2018.16
ID  - Li2018/03
ER  -