| Paper ID | IVMSP-13.2 |
| Paper Title |
PD-GAN: PERCEPTUAL-DETAILS GAN FOR EXTREMELY NOISY LOW LIGHT IMAGE ENHANCEMENT |
| Authors |
Yijun Liu, Zhengning Wang, Yi Zeng, Hao Zeng, Deming Zhao, University of Electronic Science and Technology of China, China |
| Session | IVMSP-13: Image Enhancement and Restoration |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Extremely noisy low light enhancement suffers from high-level noise, loss of texture detail, and color degradation. When recovering color or illumination for images taken in a dark environment, the challenge for networks is how to balance the enhancement for noise and texture details for a good visual effect. A single network is not suitable for solving the ill-posed problem of mapping the input image's noise to the clear target in the ground truth. To solve the problems, we pro-pose perceptual-details GAN (PD-GAN) utilizing Zero-DCE to initially recover illumination and combine residual dense-block Encoder-Decoder structure to suppress noise while finely adjusting the illumination. Besides, fractional differential gradient masks are integrated into the discriminator to enhance details. Experiment results demonstrate that PD-GAN outperforms other methods on the extremely low-light image dataset. |
