| Paper ID | IVMSP-13.4 |
| Paper Title |
SYNERGIC FEATURE ATTENTION FOR IMAGE RESTORATION |
| Authors |
Chong Mou, Jian Zhang, School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, 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 |
Local and non-local attentions are both effective methods in the domain of image restoration (IR). However, most existing image restoration methods use these two strategies indiscriminately, and how to make a trade-off between local and non-local attention operations has hardly been studied. Furthermore, the commonly used pixel-based non-local operation tends to be biased during image restoration due to the image degeneration. To overcome these problems, in this paper, we propose a novel Synergic Attention Network (SAT-Net) for image restoration as an inventive attempt to combine local and non-local attention mechanisms to restore complex textures and highly repetitive details distinguishingly. We also propose an effective patch-based non-local attention method to establish more reliable long-range dependence based on 3D patches. Experimental results on synthetic image denoising, real image denoising, and compression artifact reduction tasks show that our proposed model can achieve state-of-the-art performance under objective and subjective evaluations. |
