# iHDR: Iterative HDR Imaging with Arbitrary Number of Exposures

arXiv: https://arxiv.org/abs/2505.22971 · HTML: https://mlsanity.com/paper/2505.22971/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 29 May 2025.

## Builds on (most cited first, 13 of 13)

- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Attention-guided Network for Ghost-free High Dynamic Range Imaging](https://mlsanity.com/paper/1904.10293/) (2019)
- [Deep High Dynamic Range Imaging with Large Foreground Motions](https://mlsanity.com/paper/1711.08937/) (2017)
- [HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions](https://mlsanity.com/paper/2007.01628/) (2020)
- [Ghost-free High Dynamic Range Imaging with Context-aware Transformer](https://mlsanity.com/paper/2208.05114/) (2022)
- [Towards High-quality HDR Deghosting with Conditional Diffusion Models](https://mlsanity.com/paper/2311.00932/) (2023)
- [Alignment-free HDR Deghosting with Semantics Consistent Transformer](https://mlsanity.com/paper/2305.18135/) (2023)
- [Deep Tone Mapping Operator for High Dynamic Range Images](https://mlsanity.com/paper/1908.04197/) (2019)
- [Attention-Guided Progressive Neural Texture Fusion for High Dynamic Range Image Restoration](https://mlsanity.com/paper/2107.06211/) (2021)
- [FlexHDR: Modelling Alignment and Exposure Uncertainties for Flexible HDR Imaging](https://mlsanity.com/paper/2201.02625/) (2022)
- [A Unified HDR Imaging Method with Pixel and Patch Level](https://mlsanity.com/paper/2304.06943/) (2023)
- [HDR Imaging with Spatially Varying Signal-to-Noise Ratios](https://mlsanity.com/paper/2303.17253/) (2023)
- [Spatially Varying Exposure with 2-by-2 Multiplexing: Optimality and Universality](https://mlsanity.com/paper/2306.17367/) (2023)

## Abstract (arXiv)

High dynamic range (HDR) imaging aims to obtain a high-quality HDR image by fusing information from multiple low dynamic range (LDR) images. Numerous learning-based HDR imaging methods have been proposed to achieve this for static and dynamic scenes. However, their architectures are mostly tailored for a fixed number (e.g., three) of inputs and, therefore, cannot apply directly to situations beyond the pre-defined limited scope. To address this issue, we propose a novel framework, iHDR, for iterative fusion, which comprises a ghost-free Dual-input HDR fusion network (DiHDR) and a physics-based domain mapping network (ToneNet). DiHDR leverages a pair of inputs to estimate an intermediate HDR image, while ToneNet maps it back to the nonlinear domain and serves as the reference input for the next pairwise fusion. This process is iteratively executed until all input frames are utilized. Qualitative and quantitative experiments demonstrate the effectiveness of the proposed method as compared to existing state-of-the-art HDR deghosting approaches given flexible numbers of input frames.