Name
Abstract | ||
|---|---|---|
The main challenge for single image dehazing is the lack of effective prior information for restoration. To address this issue, in this paper, we propose to generate artificial multiple shots for simulating the images captured under different haze degrees, and two context reasoning modules are developed to describe the relationship across different spatial regions and artificial shots. It brings two benefits in the inhomogeneous haze distribution. First, within one shot, the regions occluded in one location could be recovered with the help of other clear regions, which share the similar structures. Second, for the same spatial location, the regions distorted in one shot could be restored by means of other shots with clear content. We evaluate our method on different benchmark datasets. The results demonstrate that our method achieves superior performance over many state-of-the-art dehazing algorithms. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICIP40778.2020.9190799 | 2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
Keywords | DocType | ISSN |
Single image dehazing, graph convolution networks, artificial shot, multidimensional context | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haoran Wei | 1 | 7 | 6.58 |
| Qingbo Wu | 2 | 15 | 6.30 |
| Hui Li | 3 | 123 | 45.57 |
| King Ngi Ngan | 4 | 2383 | 185.21 |
| Hongliang Li | 5 | 1833 | 101.92 |
| Fanman Meng | 6 | 14 | 5.43 |