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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Deep HDR Deghosting by Motion-Attention Fusion Network.

Yifan Xiao1, Peter Veelaert1, Wilfried Philips1

  • 1Department of Telecommunications and Information Processing, IPI-IMEC, Ghent University, 9000 Ghent, Belgium.

Sensors (Basel, Switzerland)
|October 27, 2022
PubMed
Summary

This study introduces a Deep HDR Deghosting Fusion Network (DDFNet) to eliminate ghosting artifacts in high dynamic range (HDR) images caused by moving objects. The novel network effectively fuses low dynamic range (LDR) images for superior HDR image reconstruction.

Keywords:
attention moduleconvolutional neural networkhigh dynamic range imagingimage fusion

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Multi-exposure image fusion (MEF) for high dynamic range (HDR) imaging faces challenges with ghosting artifacts in dynamic scenes due to moving objects.
  • Current methods using optical flow for low dynamic range (LDR) image alignment before fusion can introduce distortions from inaccurate motion estimation, especially with large motion or occlusion.
  • Attention-based methods, while excluding misaligned regions, also discard valuable saturated details, limiting their effectiveness.

Purpose of the Study:

  • To develop an efficient Deep HDR Deghosting Fusion Network (DDFNet) that overcomes the limitations of existing MEF techniques.
  • To leverage both optical flow-based alignment and attention mechanisms for robust HDR image fusion.
  • To achieve ghost-free HDR image reconstruction even in the presence of dynamic scene elements.

Main Methods:

  • The DDFNet employs a motion estimation module to calculate optical flow between LDR images and encodes it as a flow feature.
  • It extracts correlation features between reference and non-reference LDR images using an attention mechanism.
  • An attention-based fusion module adaptably combines information from LDR inputs using both optical flow and correlation features, followed by a Dense Network decoder for HDR reconstruction.

Main Results:

  • The proposed DDFNet effectively addresses ghosting artifacts in HDR imaging.
  • The network demonstrates superior performance in fusing LDR images compared to state-of-the-art methods.
  • Experimental results on public datasets validate the effectiveness of the DDFNet for ghost-free HDR image fusion.

Conclusions:

  • The DDFNet offers an efficient and effective solution for deghosting in HDR imaging.
  • By integrating optical flow and attention-based correlation, the network achieves robust fusion of dynamic scenes.
  • The proposed method represents a significant advancement in achieving high-quality, artifact-free HDR images.