# High Dynamic Range and Super-Resolution from Raw Image Bursts

arXiv: https://arxiv.org/abs/2207.14671 · HTML: https://mlsanity.com/paper/2207.14671/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 74 references that are in the map.
- First posted on arXiv 29 Jul 2022.

## Builds on (most cited first, 25 of 25)

- [Automatic differentiation in machine learning: a survey](https://mlsanity.com/paper/1502.05767/) (2015)
- [PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models](https://mlsanity.com/paper/2003.03808/) (2020)
- [NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images](https://mlsanity.com/paper/2111.13679/) (2021)
- [Deep Unfolding Network for Image Super-Resolution](https://mlsanity.com/paper/2003.10428/) (2020)
- [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018)
- [Benchmarking Denoising Algorithms with Real Photographs](https://mlsanity.com/paper/1707.01313/) (2017)
- [HDR image reconstruction from a single exposure using deep CNNs](https://mlsanity.com/paper/1710.07480/) (2017)
- [Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline](https://mlsanity.com/paper/2004.01179/) (2020)
- [Replacing Mobile Camera ISP with a Single Deep Learning Model](https://mlsanity.com/paper/2002.05509/) (2020)
- [Deep High Dynamic Range Imaging with Large Foreground Motions](https://mlsanity.com/paper/1711.08937/) (2017)
- [Handheld Multi-Frame Super-Resolution](https://mlsanity.com/paper/1905.03277/) (2019)
- [Deep Burst Super-Resolution](https://mlsanity.com/paper/2101.10997/) (2021)
- [HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions](https://mlsanity.com/paper/2007.01628/) (2020)
- [Single Image HDR Reconstruction Using a CNN with Masked Features and Perceptual Loss](https://mlsanity.com/paper/2005.07335/) (2020)
- [Handheld Mobile Photography in Very Low Light](https://mlsanity.com/paper/1910.11336/) (2019)
- [Pixel Recursive Super Resolution](https://mlsanity.com/paper/1702.00783/) (2017)
- [Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications](https://mlsanity.com/paper/1904.11176/) (2019)
- [Deep Reparametrization of Multi-Frame Super-Resolution and Denoising](https://mlsanity.com/paper/2108.08286/) (2021)
- [NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results](https://mlsanity.com/paper/2106.01439/) (2021)
- [Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts](https://mlsanity.com/paper/2104.06191/) (2021)
- [Locally Non-rigid Registration for Mobile HDR Photography](https://mlsanity.com/paper/1504.01441/) (2015)
- [Convolutional Sparse Coding for High Dynamic Range Imaging](https://mlsanity.com/paper/1806.04942/) (2018)
- [How to cheat with metrics in single-image HDR reconstruction](https://mlsanity.com/paper/2108.08713/) (2021)
- [Noise-Aware Merging of High Dynamic Range Image Stacks without Camera Calibration](https://mlsanity.com/paper/2009.07975/) (2020)
- [An Analysis and Implementation of the HDR+ Burst Denoising Method](https://mlsanity.com/paper/2110.09354/) (2021)

## Built on by (most cited first, 18 of 18)

- [Neural Spline Fields for Burst Image Fusion and Layer Separation](https://mlsanity.com/paper/2312.14235/) (2023)
- [Beyond the Pixel: a Photometrically Calibrated HDR Dataset for Luminance and Color Prediction](https://mlsanity.com/paper/2304.12372/) (2023)
- [NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results](https://mlsanity.com/paper/2505.12089/) (2025)
- [CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task](https://mlsanity.com/paper/2404.14132/) (2024)
- [Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery](https://mlsanity.com/paper/2303.05879/) (2023)
- [Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition](https://mlsanity.com/paper/2404.13537/) (2024)
- [HDRT: A Large-Scale Dataset for Infrared-Guided HDR Imaging](https://mlsanity.com/paper/2406.05475/) (2024)
- [Beyond RGB: Adaptive Parallel Processing for RAW Object Detection](https://mlsanity.com/paper/2503.13163/) (2025)
- [Radiance Fields from Photons](https://mlsanity.com/paper/2407.09386/) (2024)
- [Capturing Stable HDR Videos Using a Dual-Camera System](https://mlsanity.com/paper/2507.06593/) (2025)
- [Enabling High-Quality In-the-Wild Imaging from Severely Aberrated Metalens Bursts](https://mlsanity.com/paper/2510.10083/) (2025)
- [Burst Image Super-Resolution with Base Frame Selection](https://mlsanity.com/paper/2406.17869/) (2024)
- [Collaborative Blind Image Deblurring](https://mlsanity.com/paper/2305.16034/) (2023)
- [Lightweight Improved Residual Network for Efficient Inverse Tone Mapping](https://mlsanity.com/paper/2307.03998/) (2023)
- [Fine Dense Alignment of Image Bursts through Camera Pose and Depth Estimation](https://mlsanity.com/paper/2312.05190/) (2023)
- [RAW-Domain Degradation Models for Realistic Smartphone Super-Resolution](https://mlsanity.com/paper/2603.12493/) (2026)
- [Lucky High Dynamic Range Smartphone Imaging](https://mlsanity.com/paper/2604.19976/) (2026)
- [FreqAdapt: Frequency-Adaptive Processing for RAW Object Detection](https://mlsanity.com/paper/2608.03385/) (2026)

## Abstract (arXiv)

Photographs captured by smartphones and mid-range cameras have limited spatial resolution and dynamic range, with noisy response in underexposed regions and color artefacts in saturated areas. This paper introduces the first approach (to the best of our knowledge) to the reconstruction of high-resolution, high-dynamic range color images from raw photographic bursts captured by a handheld camera with exposure bracketing. This method uses a physically-accurate model of image formation to combine an iterative optimization algorithm for solving the corresponding inverse problem with a learned image representation for robust alignment and a learned natural image prior. The proposed algorithm is fast, with low memory requirements compared to state-of-the-art learning-based approaches to image restoration, and features that are learned end to end from synthetic yet realistic data. Extensive experiments demonstrate its excellent performance with super-resolution factors of up to × 4 on real photographs taken in the wild with hand-held cameras, and high robustness to low-light conditions, noise, camera shake, and moderate object motion.