Name
Title | ||
|---|---|---|
Edge Prior and Spatial Attention Fusion Enhanced Hierarchical Multi-Patch Network for Image Deblurring |
Abstract | ||
|---|---|---|
How to exploit useful features to enhance the quality of blurred images is a long-standing topic in single image deblurring. Existing learning-based approaches show exciting performance by increasing the receptive fields depending on multi-scale and scale recurrent strategy. However, it is still a challenging task for deblurring to enlarge the receptive field only relying on increasing the number of layers of a neural network. To tackle this challenge, we propose a multi-scale spatial and edge attention enhanced model (MSEA) for image deblurring. Firstly, edge features are extracted to guide the network's attention to the recovery of fine details and texture information. Then we introduce spatial attention fusion mechanism for the adaptive fusion of features derived from edge maps and blurry images, and those representing shallow fine-grained details and in-depth abstract features. Qualitative and quantitative evaluation results over GoPro and VideoDeblurring datasets demonstrated the improved performance, especially when there are sharp edges and rich textures. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9534256 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
Keywords | DocType | ISSN |
Image Deblurring, Edge Features, Spatial Attention, CNN | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yafeng Zhao | 1 | 0 | 0.68 |
| Hui Cui | 2 | 7 | 8.76 |
| Binyu Zhao | 3 | 0 | 1.01 |
| Jiquan Ma | 4 | 2 | 2.22 |