MPDNet: An underwater image deblurring framework with stepwise feature refinement module

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.

Automated summary

This study proposes a multi-progressive image deblurring network to correct blurring artifacts and local details in underwater images. A deformable convolution module is designed to address nonuniform image distortion and enrich image representation. With a stepwise feature refinement module, the network reduces the loss of contextual information and generates more realistic underwater images. Experimental evaluations show excellent results and comparisons with state-of-the-art algorithms demonstrate significant improvement in deblur performance. Ablation experiments further validate the effectiveness of all the modules in the proposed framework.