Name
Abstract | ||
|---|---|---|
Although image defogging is widely used in many working systems, existing defogging methods have some limitations due to the lack of enough information to solve the equation of fog formation model. To overcome the limitations, a novel defogging parameter selection algorithm based on artificial fish swarm algorithm (AFSA) is proposed in this paper. Two representative defogging algorithms are used to test the effectiveness of the method. The proposed method first selects the two main parameters and then optimizes them using the AFS algorithm. An assessment index of image defogging effect is used as the food concentration of the AFSA. Thus, these parameters may be adaptively and automatically adjusted for the defogging algorithms. A comparative study and qualitative evaluation demonstrate that better quality results are obtained by using the proposed method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-95930-6_4 | INTELLIGENT COMPUTING THEORIES AND APPLICATION, PT I |
Keywords | Field | DocType |
AFSA, Defogging effect assessment, Parameter selection, Single image defogging | Pattern recognition,Swarm behaviour,Computer science,Selection algorithm,Algorithm,Artificial intelligence | Conference |
Volume | ISSN | Citations |
10954 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Guo | 1 | 3 | 3.42 |
| Gonghao Lan | 2 | 1 | 0.36 |
| Xiaoming Xiao | 3 | 9 | 2.52 |
| Beiji Zou | 4 | 231 | 41.61 |