Name
Abstract | ||
|---|---|---|
Object detection in low-light aerial images is a challenging problem due to considerable variation in brightness and varying contrast. Deep learning-based approaches have recently demonstrated great promise in image enhancement. Many existing neural networks used for image quality enhancement first encode the input into low-resolution representations and then decode these representations back to a higher resolution for the contextual information. However, this method leads to the loss of semantic content. Recent research has demonstrated the advantage of maintaining high-resolution information along with lower resolution representations, which maintains image features throughout the network. In this letter, we propose a novel architecture named RNet for low-light image enhancement of aerial images. The proposed network contains multiresolution branches for better understanding of different levels of local and global context through different streams. The performance of RNet is evaluated on a recent synthetic dataset. We also present a comprehensive evaluation with a representative set of state-of-the-art enhancement techniques and neural net architectures. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2022.3181106 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Image resolution, Image enhancement, Streaming media, Training, Lighting, Feature extraction, Measurement, Deep learning, image enhancement, low-light vision, unmanned aerial vehicle (UAV) | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anirudh Singh | 1 | 0 | 0.34 |
| Amit Chougule | 2 | 0 | 0.34 |
| Pratik Narang | 3 | 60 | 11.31 |
| Vinay Chamola | 4 | 0 | 0.34 |
| Fei Yu | 5 | 5116 | 335.58 |