A novel dynamic scene deblurring framework based on hybrid activation and edge-assisted dual-branch residuals

Existing learning-based image deblurring algorithms tend to focus on single source of image information, and the network structure and dynamic scene blur characteristics make it difficult to recover the missing details of the image. Therefore, a novel dynamic scene deblurring framework is proposed based on hybrid activation and edge-assisted dual-branch residuals. Specifically, the network’s ability to learn nonlinear features is enhanced by different activation functions, and the feature utilization at different semantic levels is improved by improving the traditional residual structure. In particular, the fixed-parameter training method is adopted to reduce ringing artifacts. And a new dual-source edge extraction algorithm is designed that organically combines edge information from different sources as network inputs. The experimental results demonstrate that our algorithm not only shows advantages in objective evaluation metrics PSNR, SSIM and VIF, but also achieves satisfactory results in subjective visual effects. Source code is publicly available at: https://github.com/Mangolzh/HN.git.