Name
Abstract | ||
|---|---|---|
The dehazing techniques designed so far are not so-effective at preserving texture details, especially in case of a complex background and large haze gradient image. Therefore, the exploration of new alternatives for designing an effective prior is desirable. Thus, in this research work, Gradient profile prior (GPP) is designed to evaluate depth map from hazy images. The transmission map is also improved by utilizing Guided anisotropic diffusion and iterative learning based image filter (GADILF). The restoration model is also improved to reduce the effect of pixels saturation and color distortion from restored images. Performance analysis demonstrates that GPP can naturally restore the hazy image especially at the edges of sudden changes in the obtained depth map. Through extensive analysis, it has been found that GPP based dehazing can effectively suppress visual artefacts for hazy images and yield high-quality results as compared to the competitive dehazing techniques both quantitatively and qualitatively. Moreover, the relatively high computational speed of the proposed technique will facilitate it in real-time applications. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s10489-019-01504-6 | Applied Intelligence |
Keywords | Field | DocType |
Haze removal, Gradient channel prior, Restoration model, Transmission map, GADILF | Anisotropic diffusion,Pattern recognition,Computer science,Communication channel,Composite image filter,Artificial intelligence,Pixel,Depth map,Iterative learning control,Distortion,Haze | Journal |
Volume | Issue | ISSN |
49 | 12 | 0924-669X |
Citations | PageRank | References |
1 | 0.37 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dilbag Singh | 1 | 67 | 15.16 |
| Vijay Kumar | 2 | 229 | 21.59 |
| Manjit Kaur | 3 | 23 | 8.41 |