HistoHDR-Net: Histogram Equalization for Single LDR to HDR Image Translation
Not on the map. Why?
Abstract (arXiv)
High Dynamic Range (HDR) imaging aims to replicate the high visual quality and clarity of real-world scenes. Due to the high costs associated with HDR imaging, the literature offers various data-driven methods for HDR image reconstruction from Low Dynamic Range (LDR) counterparts. A common limitation of these approaches is missing details in regions of the reconstructed HDR images, which are over- or under-exposed in the input LDR images. To this end, we propose a simple and effective method, HistoHDR-Net, to recover the fine details (e.g., color, contrast, saturation, and brightness) of HDR images via a fusion-based approach utilizing histogram-equalized LDR images along with self-attention guidance. Our experiments demonstrate the efficacy of the proposed approach over the state-of-art methods.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 22 of its 48 references that are in the map. First posted on arXiv 8 Feb 2024.
Builds on
22 of 48 references.
References · 22
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10 more
- 2021Deep Learning for HDR Imaging: State-of-the-Art and Future Trendscited by 45
- 2017Learning High Dynamic Range from Outdoor Panoramascited by 34
- 2023RawHDR: High Dynamic Range Image Reconstruction from a Single Raw Imagecited by 31
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- 2022Single-Image HDR Reconstruction by Multi-Exposure Generationcited by 20
- 2023Single Image LDR to HDR Conversion using Conditional Diffusioncited by 9
- 2023Self-Supervised High Dynamic Range Imaging with Multi-Exposure Images in Dynamic Scenescited by 8
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Built on by
Citing papers per year: 2024: 2; 2025: 1.
3 papers.
- 2025PhysHDR: When Lighting Meets Materials and Scene Geometry in HDR Reconstruction21 Sep 2025
- 2024A Cycle Ride to HDR: Semantics Aware Self-Supervised Framework for Unpaired LDR-to-HDR Image Reconstruction19 Oct 2024
- 2024GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction26 Mar 2024