Name
Abstract | ||
|---|---|---|
Camera sensors often fail to capture clear images or videos in a poorly-lit environment. In this paper, we propose a trainable hybrid network to enhance the visibility of such degraded images. The proposed network consists of two distinct streams to simultaneously learn the global content and salient structures of the clear image in a unified network. More specifically, the content stream estimates the global content of the low-light input through an encoder-decoder network. However, the encoder in the content stream tends to lose some structure details. To remedy this, we propose a novel spatially variant recurrent neural network (RNN) as an edge stream to model edge details, with the guidance of another auto-encoder. Experimental results show that the proposed network performs favorably against the state-of-the-art low-light image enhancement algorithms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TIP.2019.2910412 | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
Keywords | Field | DocType |
Low-light image enhancement,convolutional neural network,recurrent neural network | Computer vision,Visibility,Pattern recognition,Image sensor,Convolutional neural network,Recurrent neural network,Encoder,Artificial intelligence,Mathematics,Salient | Journal |
Volume | Issue | ISSN |
28 | 9 | 1941-0042 |
Citations | PageRank | References |
23 | 0.63 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenqi Ren | 1 | 335 | 27.14 |
| Sifei Liu | 2 | 227 | 17.54 |
| Lin Ma | 3 | 912 | 71.35 |
| Qianqian Xu | 4 | 160 | 22.98 |
| Xiangyu Xu | 5 | 143 | 5.66 |
| Xiaochun Cao | 6 | 1986 | 131.55 |
| Junping Du | 7 | 789 | 91.80 |
| Yang Ming-Hsuan | 8 | 15303 | 620.69 |