Example based depth from fog
Research output: Contribution to journal › Conference article › Research › peer-review
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Example based depth from fog. / Gibson, Kristofor B.; Belongie, Serge J.; Nguyen, Truong Q.
In: 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2013, p. 728-732.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Example based depth from fog
AU - Gibson, Kristofor B.
AU - Belongie, Serge J.
AU - Nguyen, Truong Q.
PY - 2013
Y1 - 2013
N2 - The presence of fog in an image reduces contrast which can be considered a nuisance in imaging applications, however, we consider this useful information for image enhancement and scene understanding. In this paper, we present a new method for estimating depth from fog in a single image and single image fog removal. We use an example based approach that is trained from data with known fog and depth. A data driven method and physics based model are used to develop the example based learning framework for single image fog removal. In addition, we account for various colors of fog by using a linear transformation of the RGB colorspace. This approach has the flexibility to learn from various scenes and relaxes the common constraint of fixed camera position. We present depth estimations and fog removal from a single image with good results.
AB - The presence of fog in an image reduces contrast which can be considered a nuisance in imaging applications, however, we consider this useful information for image enhancement and scene understanding. In this paper, we present a new method for estimating depth from fog in a single image and single image fog removal. We use an example based approach that is trained from data with known fog and depth. A data driven method and physics based model are used to develop the example based learning framework for single image fog removal. In addition, we account for various colors of fog by using a linear transformation of the RGB colorspace. This approach has the flexibility to learn from various scenes and relaxes the common constraint of fixed camera position. We present depth estimations and fog removal from a single image with good results.
KW - Contrast Enhancement
KW - Data Driven
KW - Depth from Fog
KW - Visibility
UR - http://www.scopus.com/inward/record.url?scp=84897749419&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2013.6738150
DO - 10.1109/ICIP.2013.6738150
M3 - Conference article
AN - SCOPUS:84897749419
SP - 728
EP - 732
JO - 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
JF - 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
T2 - 2013 20th IEEE International Conference on Image Processing, ICIP 2013
Y2 - 15 September 2013 through 18 September 2013
ER -
ID: 302046810