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Pyramid Inter-Attention for High Dynamic Range Imaging.

Sungil Choi1, Jaehoon Cho1, Wonil Song1

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Summary
This summary is machine-generated.

This study introduces a new method for high-dynamic-range (HDR) imaging that effectively removes ghosting artifacts caused by significant motion in dynamic scenes. The novel approach ensures clear HDR images even with severe misalignment in low-dynamic-range inputs.

Keywords:
HDR imagingattention mechanismsoptical flow

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Photography

Background:

  • High-dynamic-range (HDR) imaging aims to capture a greater range of luminance than standard imaging.
  • Dynamic scenes with significant object or camera motion introduce severe misalignment in low-dynamic-range (LDR) images, leading to ghosting artifacts in traditional HDR reconstruction.
  • Existing non-flow-based and flow-based methods struggle to mitigate these ghosting artifacts effectively due to alignment errors.

Purpose of the Study:

  • To propose a novel approach for generating ghost-free HDR images from dynamic scenes with severe misalignment.
  • To develop an effective alignment network capable of addressing substantial motion-induced misalignments in LDR image sequences.
  • To enhance the feature representation during the merging process for improved HDR image quality.

Main Methods:

  • A novel alignment network is proposed, featuring a pyramid inter-attention module (PIAM) that utilizes inter-attention maps for aligning LDR image features.
  • A dual excitation block (DEB) is introduced to recalibrate aligned features spatially and channel-wise, boosting their representational power during the merging stage.
  • The method focuses on direct feature alignment rather than relying on potentially erroneous optical flow estimations.

Main Results:

  • The proposed PIAM effectively aligns features in LDR images, even under severe misalignment conditions.
  • The DEB enhances the representation of aligned features, contributing to artifact reduction.
  • Experimental results demonstrate that the combined approach achieves state-of-the-art performance in producing ghost-free HDR images from dynamic scenes.

Conclusions:

  • The proposed PIAM and DEB offer a simple yet effective solution for eliminating ghosting artifacts in HDR imaging of dynamic scenes.
  • This novel approach significantly improves the quality of HDR images by accurately handling severe misalignment.
  • The method represents a substantial advancement in ghost-free HDR imaging for challenging dynamic scenarios.