Name
Abstract | ||
|---|---|---|
Removing the haze from still images is a challenging issue. Dark Channel Prior (DCP) based dehazing techniques have been used to remove haze from still images. However, it produces poor results when image objects are inherently similar to the airlight and no shadow is cast on them. To eliminate this problem, a Moore neighborhood-based gradient profile prior is designed and developed to efficiently estimate the transmission map and atmospheric veil. The transmission map is also refined by developing a local activity-tuned anisotropic diffusion based filter. Afterward, image restoration is performed using the estimated transmission function. Thus, the proposed technique has an ability to remove haze from still images in an effective manner. The performance of the proposed technique is compared with recently developed seven dehazing techniques over synthetic and real-life hazy images. The experimental results depict the supremacy of the proposed technique in removing haze from still images when compared with several existing techniques. It also reveals that the restored image has little or no artifacts. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.image.2018.09.011 | Signal Processing: Image Communication |
Keywords | Field | DocType |
Hazy images,Moore neighborhood-based gradient profile prior,Local activity-tuned anisotropic diffusion,Transmission map | Anisotropic diffusion,Computer vision,Shadow,Moore neighborhood,Computer science,Communication channel,Artificial intelligence,Image restoration,Haze | Journal |
Volume | ISSN | Citations |
70 | 0923-5965 | 1 |
PageRank | References | Authors |
0.34 | 28 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dilbag Singh | 1 | 67 | 15.16 |
| Vijay Kumar | 2 | 1 | 0.34 |