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MPDNet: An underwater image deblurring framework with stepwise feature refinement module.
- Source :
-
Engineering Applications of Artificial Intelligence . Nov2023:Part A, Vol. 126, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- In this study, a general network model called multi-progressive image deblurring network is proposed to correct blurring artifacts and local imaging details in underwater images. As a solution to nonuniform image distortion, a deformable convolution module was designed to enrich the encoded information of the image representation. Using a stepwise feature refinement module, multi-progressive image deblurring network can reduce the loss of contextual information to produce a more realistic underwater image for subsequent applications. Constructing a loss function based on multi-scale content can help the model improve image perception quality. We conducted experimental evaluations on large-scale image deblurring benchmark datasets, such as GoPro and HIDE, achieving excellent results with 32.84 dB and 31.03 dB peak signal-to-noise ratio, respectively, using the proposed method. Subsequently, a detailed optimization comparison was conducted on the in-house underwater image deblurring dataset. Multi-progressive image deblurring network obtained higher-quality, clearer images. Compared with the current state-of-the-art image deblurring algorithms, the proposed model achieved significant results with a 6.6% increase in deblur performance in peak signal-to-noise ratio. Finally, we conducted ablation experiments to evaluate the effectiveness of all the modules in the proposed framework. • Multi-progressive structure can hierarchically fuse global context information. • MPDNet outperforms almost all the SOTA methods on the benchmark datasets. • MPDNet is enough lightweight for deblurring tasks on mobile computing platforms. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09521976
- Volume :
- 126
- Database :
- Academic Search Index
- Journal :
- Engineering Applications of Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 173473924
- Full Text :
- https://doi.org/10.1016/j.engappai.2023.106822