Name
Abstract | ||
|---|---|---|
Image dehazing has become an important computational imaging topic in the recent years. However, due to the lack of ground truth images, the comparison of dehazing methods is not straightforward, nor objective. To overcome this issue we introduce I-HAZE, a new dataset that contains 35 image pairs of hazy and corresponding haze-free (ground-truth) indoor images. Different from most of the existing dehazing databases, hazy images have been generated using real haze produced by a professional haze machine. To ease color calibration and improve the assessment of dehazing algorithms, each scene includes a MacBeth color checker. Moreover, since the images are captured in a controlled environment, both haze-free and hazy images are captured under the same illumination conditions. This represents an important advantage of the I-HAZE dataset that allows us to objectively compare the existing image dehazing techniques using traditional image quality metrics such as PSNR and SSIM. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01449-0_52 | ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, ACIVS 2018 |
DocType | Volume | ISSN |
Conference | 11182 | 0302-9743 |
Citations | PageRank | References |
21 | 0.75 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Codruta Orniana Ancuti | 1 | 304 | 20.86 |
| Cosmin Ancuti | 2 | 314 | 22.39 |
| Radu Timofte | 3 | 1880 | 118.45 |
| Christophe De Vleeschouwer | 4 | 392 | 44.86 |