Name
Abstract | ||
|---|---|---|
Bad weather (eg., fog and haze) significantly degrades the quality of outdoor images taken by camera, leading to the fact that most automatic systems, which strongly depends on the definition of the input images, fail to work normally. Thus, the improvement of the dehazing technology is highly desired. To overcome the disadvantages of traditional dark channel priorbased algorithm, we propose a more efficient dehazing algorithm combining dark channel prior and mean shift segmentation. Firstly, we take the operation of white balance on the input haze image to reduce the negative influence of color cast. Secondly, we use the mean shift segmentation algorithm to separate the sky regions from the foreground in the transmission map, which is obtained with the dark channel prior-based approach. Thirdly, we enhance the brightness of the sky regions in the transmission map independently and use guided image filtering to smooth the map. Finally, we restore the image with the re-refined transmission map. The experimental results demonstrate that the proposed approach is better able to handle the sky regions and solve the problem of color cast compared with the typical dehazing algorithms. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SMC.2014.6974568 | SMC |
Keywords | Field | DocType |
bad weather,automatic systems,re-refined transmission map,outdoor images,white balance,mean shift-based single image dehazing,image segmentation,image denoising,image restoration,image filtering,dark channel priorbased algorithm,dehazing algorithms,image enhancement,dehazing technology,haze image,mean shift segmentation algorithm | Computer vision,Scale-space segmentation,Image texture,Non-local means,Computer science,Binary image,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Mean-shift,Image restoration | Conference |
ISSN | Citations | PageRank |
1062-922X | 0 | 0.34 |
References | Authors | |
16 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingsong Zhu | 1 | 116 | 13.96 |
| Jiaming Mai | 2 | 0 | 0.34 |
| Zhan Song | 3 | 78 | 12.58 |
| Di Wu | 4 | 636 | 117.73 |
| Jianjun Wang | 5 | 53 | 11.84 |
| Lei Wang | 6 | 25 | 2.48 |