Name
Abstract | ||
|---|---|---|
To enhance the visibility and usability of images captured in hazy conditions, many image dehazing algorithms (DHAs) have been proposed. With so many image DHAs, there is need to evaluate and compare these DHAs. Due to the lack of the reference haze-free images, DHAs are generally evaluated qualitatively using real hazy images. But it is possible to perform quantitative evaluation using synthetic hazy images since the reference haze-free images are available and full-reference (FR) image quality assessment (IQA) measures can be utilized. In this paper, we follow this strategy and study DHA evaluation using synthetic hazy images systematically. We first build a synthetic haze removing quality (SHRQ) database. It consists of two subsets: regular and aerial image subsets, which include 360 and 240 dehazed images created from 45 and 30 synthetic hazy images using 8 DHAs, respectively. Since aerial imaging is an important application area of dehazing, we create an aerial image subset specifically. We then carry out subjective quality evaluation study on these two subsets. We observe that taking DHA evaluation as an exact FR IQA process is questionable, and the state-of-the-art FR IQA measures are not effective for DHA evaluation. Thus we propose a DHA quality evaluation method by integrating some dehazing-relevant features, including image structure recovering, color rendition, and over-enhancement of low-contrast areas. The proposed method works for both types of images, but we further improve it for aerial images by incorporating its specific characteristics. Experimental results on two subsets of the SHRQ database validate the effectiveness of the proposed measures. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tmm.2019.2902097 | IEEE Transactions on Multimedia |
Keywords | Field | DocType |
Atmospheric modeling,Image color analysis,Atmospheric measurements,Databases,Scattering,Computational modeling,Imaging | Computer vision,Visibility,Pattern recognition,Computer science,Usability,Image quality,Aerial image,Atmospheric measurements,Artificial intelligence,Image structure | Journal |
Volume | Issue | ISSN |
21 | 9 | 1520-9210 |
Citations | PageRank | References |
5 | 0.37 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiongkuo Min | 1 | 337 | 40.88 |
| Guangtao Zhai | 2 | 1707 | 145.33 |
| Ke Gu | 3 | 1321 | 77.21 |
| Yucheng Zhu | 4 | 35 | 5.71 |
| Jiantao Zhou | 5 | 580 | 78.87 |
| Guodong Guo | 6 | 2548 | 144.00 |
| Xiaokang Yang | 7 | 3581 | 238.09 |
| Xinping Guan | 8 | 2791 | 253.38 |
| Wenjun Zhang | 9 | 1789 | 177.28 |