Name
Abstract | ||
|---|---|---|
Single image de-raining is an important and highly challenging problem. To address this problem, some depth or density guided single-image de-raining methods have been developed with encouraging performance. However, these methods individually use the depth or the density to guide the network to conduct image de-raining. In this paper, a novel
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">joint depth and density guided de-raining</i>
(JDDGD) method is technically developed. The JDDGD starts with a
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">depth-density inference network</i>
(DDINet) to extract the depth and density information from an input rainy image, followed by a
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">depth-density-based</i>
conditional generative adversarial network (DD-CGAN) to exploit the depth and density information provided by DDINet to achieve adaptive rain streak and fog removal. To prevent the spatially-varying local artifacts, an effective
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">global-local discriminators</i>
structure is introduced in the proposed DD-CGAN to globally and locally inspect the generated images. In addition, multiple loss functions including
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-scale pixel loss</i>
,
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-scale perceptual loss</i>
, and
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">global-local generative adversarial loss</i>
are also jointly used to train our model to achieve the best performance. Both quantitative and qualitative results show that the proposed JDDGD method achieves superior performance than previous
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-guided</i>
,
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">density-guided</i>
, and
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">depth-guided de-raining</i>
methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCSVT.2021.3121012 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | DocType | Volume |
Single image de-raining,depth-density inference network,depth-density-based conditional generative adversarial network,global-local discriminators | Journal | 32 |
Issue | ISSN | Citations |
7 | 1051-8215 | 0 |
PageRank | References | Authors |
0.34 | 28 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Cai | 1 | 53 | 19.97 |
| Yuli Fu | 2 | 200 | 29.90 |
| Tao Zhu | 3 | 7 | 4.13 |
| Youjun Xiang | 4 | 4 | 2.09 |
| Ying Zhang | 5 | 0 | 0.34 |
| Huanqiang Zeng | 6 | 395 | 36.94 |