# Deep Chain HDRI: Reconstructing a High Dynamic Range Image from a Single Low Dynamic Range Image

arXiv: https://arxiv.org/abs/1801.06277 · HTML: https://mlsanity.com/paper/1801.06277/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2018.

## Built on by (most cited first, 25 of 25)

- [Deep Learning for HDR Imaging: State-of-the-Art and Future Trends](https://mlsanity.com/paper/2110.10394/) (2021)
- [Text2Light: Zero-Shot Text-Driven HDR Panorama Generation](https://mlsanity.com/paper/2209.09898/) (2022)
- [GlowGAN: Unsupervised Learning of HDR Images from LDR Images in the Wild](https://mlsanity.com/paper/2211.12352/) (2022)
- [Single-Image HDR Reconstruction by Multi-Exposure Generation](https://mlsanity.com/paper/2210.15897/) (2022)
- [Luminance Attentive Networks for HDR Image and Panorama Reconstruction](https://mlsanity.com/paper/2109.06688/) (2021)
- [Bracket Diffusion: HDR Image Generation by Consistent LDR Denoising](https://mlsanity.com/paper/2405.14304/) (2024)
- [Beyond the Pixel: a Photometrically Calibrated HDR Dataset for Luminance and Color Prediction](https://mlsanity.com/paper/2304.12372/) (2023)
- [Casual Indoor HDR Radiance Capture from Omnidirectional Images](https://mlsanity.com/paper/2208.07903/) (2022)
- [Learning Continuous Exposure Value Representations for Single-Image HDR Reconstruction](https://mlsanity.com/paper/2309.03900/) (2023)
- [A Two-stage Deep Network for High Dynamic Range Image Reconstruction](https://mlsanity.com/paper/2104.09386/) (2021)
- [AIM 2025 challenge on Inverse Tone Mapping Report: Methods and Results](https://mlsanity.com/paper/2508.13479/) (2025)
- [HDR-cGAN: Single LDR to HDR Image Translation using Conditional GAN](https://mlsanity.com/paper/2110.01660/) (2021)
- [LHDR: HDR Reconstruction for Legacy Content using a Lightweight DNN](https://mlsanity.com/paper/2211.11270/) (2022)
- [Deep HDR Hallucination for Inverse Tone Mapping](https://mlsanity.com/paper/2106.09486/) (2021)
- [HDRGS: High Dynamic Range Gaussian Splatting](https://mlsanity.com/paper/2408.06543/) (2024)
- [GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction](https://mlsanity.com/paper/2403.17837/) (2024)
- [DeepHS-HDRVideo: Deep High Speed High Dynamic Range Video Reconstruction](https://mlsanity.com/paper/2210.04429/) (2022)
- [Efficient HDR Reconstruction from Real-World Raw Images](https://mlsanity.com/paper/2306.10311/) (2023)
- [Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas](https://mlsanity.com/paper/2103.12926/) (2021)
- [SJ-HD^2R: Selective Joint High Dynamic Range and Denoising Imaging for Dynamic Scenes](https://mlsanity.com/paper/2206.09611/) (2022)
- [Deep Polarimetric HDR Reconstruction](https://mlsanity.com/paper/2203.14190/) (2022)
- [A Mixed Quantization Network for Computationally Efficient Mobile Inverse Tone Mapping](https://mlsanity.com/paper/2203.06504/) (2022)
- [Supervised Image Segmentation for High Dynamic Range Imaging](https://mlsanity.com/paper/2212.03002/) (2022)
- [High Dynamic Range Imaging via Visual Attention Modules](https://mlsanity.com/paper/2307.14705/) (2023)
- [Recurrent Dynamic Range Extension](https://mlsanity.com/paper/2609.13135/) (2026)

## Abstract (arXiv)

In this paper, we propose a novel deep neural network model that reconstructs a high dynamic range (HDR) image from a single low dynamic range (LDR) image. The proposed model is based on a convolutional neural network composed of dilated convolutional layers, and infers LDR images with various exposures and illumination from a single LDR image of the same scene. Then, the final HDR image can be formed by merging these inference results. It is relatively easy for the proposed method to find the mapping between the LDR and an HDR with a different bit depth because of the chaining structure inferring the relationship between the LDR images with brighter (or darker) exposures from a given LDR image. The method not only extends the range, but also has the advantage of restoring the light information of the actual physical world. For the HDR images obtained by the proposed method, the HDR-VDP2 Q score, which is the most popular evaluation metric for HDR images, was 56.36 for a display with a 1920×1200 resolution, which is an improvement of 6 compared with the scores of conventional algorithms. In addition, when comparing the peak signal-to-noise ratio values for tone mapped HDR images generated by the proposed and conventional algorithms, the average value obtained by the proposed algorithm is 30.86 dB, which is 10 dB higher than those obtained by the conventional algorithms.