mlsanityData to 14 Oct 2026

Single-Image HDR Reconstruction Assisted Ghost Suppression and Detail Preservation Network for Multi-Exposure HDR Imaging

arXiv 2403.04228 ↗First posted 7 Mar 2024cs.CV eess.IVcited by 3

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Abstract (arXiv)

The reconstruction of high dynamic range (HDR) images from multi-exposure low dynamic range (LDR) images in dynamic scenes presents significant challenges, especially in preserving and restoring information in oversaturated regions and avoiding ghosting artifacts. While current methods often struggle to address these challenges, our work aims to bridge this gap by developing a multi-exposure HDR image reconstruction network for dynamic scenes, complemented by single-frame HDR image reconstruction. This network, comprising single-frame HDR reconstruction with enhanced stop image (SHDR-ESI) and SHDR-ESI-assisted multi-exposure HDR reconstruction (SHDRA-MHDR), effectively leverages the ghost-free characteristic of single-frame HDR reconstruction and the detail-enhancing capability of ESI in oversaturated areas. Specifically, SHDR-ESI innovatively integrates single-frame HDR reconstruction with the utilization of ESI. This integration not only optimizes the single image HDR reconstruction process but also effectively guides the synthesis of multi-exposure HDR images in SHDR-AMHDR. In this method, the single-frame HDR reconstruction is specifically applied to reduce potential ghosting effects in multiexposure HDR synthesis, while the use of ESI images assists in enhancing the detail information in the HDR synthesis process. Technically, SHDR-ESI incorporates a detail enhancement mechanism, which includes a self-representation module and a mutual-representation module, designed to aggregate crucial information from both reference image and ESI. To fully leverage the complementary information from non-reference images, a feature interaction fusion module is integrated within SHDRA-MHDR. Additionally, a ghost suppression module, guided by the ghost-free results of SHDR-ESI, is employed to suppress the ghosting artifacts.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 24 of its 60 references that are in the map. First posted on arXiv 7 Mar 2024.

Builds on

24 of 60 references.

References · 24

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2017HDR image reconstruction from a single exposure using deep CNNscited by 167
  3. 2020Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipelinecited by 132
  4. 2019Attention-guided Network for Ghost-free High Dynamic Range Imagingcited by 117
  5. 2017Deep High Dynamic Range Imaging with Large Foreground Motionscited by 99
  6. 2018ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Contentcited by 89
  7. 2020HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motionscited by 80
  8. 2022Ghost-free High Dynamic Range Imaging with Context-aware Transformercited by 65
  9. 2021HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantizationcited by 58
  10. 2021ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imagingcited by 51
  11. 2023Towards High-quality HDR Deghosting with Conditional Diffusion Modelscited by 33
  12. 2018Image Correction via Deep Reciprocating HDR Transformationcited by 30
12 more
  1. 2021Attention-Guided Progressive Neural Texture Fusion for High Dynamic Range Image Restorationcited by 23
  2. 2019FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Networkcited by 22
  3. 2022Single-Image HDR Reconstruction by Multi-Exposure Generationcited by 20
  4. 2022FlexHDR: Modelling Alignment and Exposure Uncertainties for Flexible HDR Imagingcited by 17
  5. 2023A Unified HDR Imaging Method with Pixel and Patch Levelcited by 17
  6. 2020End-to-End Differentiable Learning to HDR Image Synthesis for Multi-exposure Imagescited by 15
  7. 2021How to cheat with metrics in single-image HDR reconstructioncited by 11
  8. 2023SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoderscited by 11
  9. 2020HDR Imaging with Quanta Image Sensors: Theoretical Limits and Optimal Reconstructioncited by 9
  10. 2018Learning an Inverse Tone Mapping Network with a Generative Adversarial Regularizercited by 6
  11. 2021Self-Gated Memory Recurrent Network for Efficient Scalable HDR Deghostingcited by 5
  12. 2023High Dynamic Range Imaging of Dynamic Scenes with Saturation Compensation but without Explicit Motion Compensationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.