| Paper ID | IVMSP-20.5 |
| Paper Title |
NASA: A Noise-Adaptive and Structure-Aware Learning Framework for Image Deblurring |
| Authors |
Xiaokun Liu, Long Ma, Risheng Liu, Wei Zhong, Xin Fan, Zhongxuan Luo, Dalian University of Technology, China |
| Session | IVMSP-20: Denoising and Deblurring |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation Time: | Thursday, 10 June, 13:00 - 13:45 |
| 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 |
Image deblurring is a classical low-level visual processing task, which aims to recover a potentially noise-free sharp image from the blurred image. Existing prior-based and learning-based methods usually need to manually set some vital auxiliary components (e.g., noise level). It brings about extremely weak adaptability and flexibility. To settle this issue, we develop a Noise-Adaptive Structure-Aware learning framework (NASA) to achieve fully intelligent manufacturing. Concretely, by introducing a new task-assisted module, we define a novel robust image deblurring model derived from a MAP-based energy function. Consequently, we establish the NASA which consists of three basic modules including the task-assisted, fidelity-term, and regularization-term modules, to solve our designed model. The task-assisted module generates the noise-adaptive and structure-aware maps, which are fed to the other two modules. By end-to-end training our NASA, we successfully avoid the cumbersome manually parameters-adjustment process. Quantitative and qualitative experiments demonstrate our superiority compared to the state-of-the-art methods, both in visual effect and numerical scores. A series of ablation study also verify the effectiveness and necessity of our designed mechanism. |
