Example based depth from fog
Research output: Contribution to journal › Conference article › Research › peer-review
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.
Original language | English |
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Journal | 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings |
Pages (from-to) | 728-732 |
Number of pages | 5 |
DOIs | |
Publication status | Published - 2013 |
Externally published | Yes |
Event | 2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia Duration: 15 Sep 2013 → 18 Sep 2013 |
Conference
Conference | 2013 20th IEEE International Conference on Image Processing, ICIP 2013 |
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Country | Australia |
City | Melbourne, VIC |
Period | 15/09/2013 → 18/09/2013 |
Sponsor | The Institute of Electrical and Electronics Engineers (IEEE) Signal Processing Society |
- Contrast Enhancement, Data Driven, Depth from Fog, Visibility
Research areas
ID: 302046810