SDAN-MD: Supervised dual attention network for multi-stage motion deblurring in frontal-viewing vehicle-camera images

Seong In Jeong, Min Su Jeong, Seon Jong Kang, Kyung Bong Ryu, Kang Ryoung Park

Research output: Contribution to journalArticlepeer-review

Abstract

Motion blur in images usually distorts the information of objects, thus degrading the performance of semantic segmentation. However, there is no previous research on improving the segmentation performance by restoring the frontal-viewing vehicle-camera images taken under motion blur. Therefore, this study proposes a supervised dual attention network for multi-stage motion deblurring (SDAN-MD) for this task. In SDAN-MD, a supervised dual attention module (SDAM) is proposed, which adopts the supervised spatial and channel attention mechanisms to provide a supervisory signal of ground truth. In addition to Charbonnier loss and edge loss, we use perceptual loss utilizing Euclidean distance based on feature maps obtained from the segmentation network. Experiments were conducted with the motion blurred databases from the two open databases of road scene, Cambridge driving Labeled Video Database (CamVid) and Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI).The results show that the proposed SDAN-MD achieves 92.89% and 87.27% pixel accuracies in semantic segmentation using these two databases, respectively, outperforming the state-of-the-art methods.

Original languageEnglish
Article number101556
JournalJournal of King Saud University - Computer and Information Sciences
Volume35
Issue number5
DOIs
StatePublished - May 2023

Keywords

  • Motion blur
  • Multi-stage
  • Perceptual loss
  • Semantic segmentation
  • Supervised dual attention module

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