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- Title
Deformable multi-scale fusion network for non-uniform single image deblurring.
- Authors
Zhang, Zhizhou; Chen, Yang; Zhu, Aichun; Liu, Hanxi
- Abstract
Non-uniform image deblurring is an ill-posed problem. Previous research efforts attempt to solve this problem by increasing the number of scales processed in the model, including but not limited to multi-scale methods, multi-patch methods, and atrous convolution. However, these methods are still subject to the fixed geometric structures, which are inherently unable to adequately handle complex blur. This paper proposes a novel residual block called Deform-ResBlock that is composed of traditional convolution and deformable convolution to enhance the model's capability of modeling geometric transformations. Then, we design parallel multi-scale convolution streams composed of densely Deform-ResBlock for extracting multi-scale features. Finally, we apply the multi-patch approach stacking two stages to deblur images gradually. The overall method is named deformable multi-scale fusion network (DMSFN). Compared to the previous methods, our method combines the advantages of multi-scale and multi-patch approaches and has better modeling geometric transformation capability. Extensive experimental results on the GoPro, HIDE, and RealBlur datasets demonstrate that the proposed method performs favorably against the state-of-the-art in the non-uniform image deblurring.
- Publication
Multimedia Tools & Applications, 2023, Vol 82, Issue 29, p45621
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-023-14818-y