Name
Abstract | ||
|---|---|---|
Image enhancement (IE) technology can help enhance the brightness of remote-sensing images to obtain better interpretation and visualization effects. Convolutional neural networks (CNN), such as the Low-light CNN (LLCNN) and Super-resolution CNN (SRCNN), have achieved great success in image enhancement, image super resolution, and other image-processing applications. Therefore, we adopt CNN to propose a new neural network architecture with end-to-end strategy for low-light remote-sensing IE, named remote-sensing CNN (RSCNN). In RSCNN, an upsampling operator is adopted to help learn more multi-scaled features. With respect to the lack of labeled training data in remote-sensing image datasets for IE, we use real natural image patches to train firstly and then perform fine-tuning operations with simulated remote-sensing image pairs. Reasonably designed experiments are carried out, and the results quantitatively show the superiority of RSCNN in terms of structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) over conventional techniques for low-light remote-sensing IE. Furthermore, the results of our method have obvious qualitative advantages in denoising and maintaining the authenticity of colors and textures. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/rs13010062 | REMOTE SENSING |
Keywords | DocType | Volume |
convolutional neural network, low-light enhancement, remote-sensing image | Journal | 13 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Linshu Hu | 1 | 0 | 1.35 |
| Mengjiao Qin | 2 | 17 | 2.50 |
| Feng Zhang | 3 | 0 | 2.03 |
| Zhenhong Du | 4 | 31 | 16.98 |
| Liu Renyi | 5 | 15 | 13.13 |