Loading...
Please wait, while we are loading the content...
Multi-Feature Guided Low-Light Image Enhancement
| Content Provider | MDPI |
|---|---|
| Author | Liang, Hong Yu, Ankang Shao, Mingwen Tian, Yuru |
| Copyright Year | 2021 |
| Description | Due to the characteristics of low signal-to-noise ratio and low contrast, low-light images will have problems such as color distortion, low visibility, and accompanying noise, which will cause the accuracy of the target detection problem to drop or even miss the detection target. However, recalibrating the dataset for this type of image will face problems such as increased cost or reduced model robustness. To solve this kind of problem, we propose a low-light image enhancement model based on deep learning. In this paper, the feature extraction is guided by the illumination map and noise map, and then the neural network is trained to predict the local affine model coefficients in the bilateral space. Through these methods, our network can effectively denoise and enhance images. We have conducted extensive experiments on the LOL datasets, and the results show that, compared with traditional image enhancement algorithms, the model is superior to traditional methods in image quality and speed. |
| Starting Page | 5055 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app11115055 |
| Journal | Applied Sciences |
| Issue Number | 11 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-05-29 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Artificial Intelligence Low Light Enhancement Feature Guided Denoise |
| Content Type | Text |
| Resource Type | Article |
