Review paper categorizes HDR imaging methods

This review paper categorizes and analyzes existing literature on multi-exposure High Dynamic Range (HDR) imaging. It focuses on two key areas: multi-exposure fusion (MEF) and ghost removal techniques. The paper examines both conventional filter-based methods and modern data-driven approaches, particularly deep learning techniques, which are further classified based on their alignment and fusion domains (pixel-space vs. feature-space). It also discusses common datasets, evaluation metrics, and outlines future research directions. AI

IMPACT Provides a structured overview of deep learning techniques in HDR imaging, potentially guiding future research and development in computer vision.

RANK_REASON The item is a review paper on a specific technical topic (HDR imaging) published on arXiv. [lever_c_demoted from research: ic=1 ai=0.7]

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• arXiv
• Attention Mechanism
• deep learning
• deformable convolutions
• feature vector
• Ghost removal method and radar device
• Multi-exposure fusion for welding region based on multi-scale transform and hybrid weight
• multi-exposure HDR capture
• Multi-exposure HDR Imaging
• optical flow
• Pixel Space Battles
• spatial transformers

• paper
• other

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