# An Analysis and Implementation of the HDR+ Burst Denoising Method

arXiv: https://arxiv.org/abs/2110.09354 · HTML: https://mlsanity.com/paper/2110.09354/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018)
- [Burst Denoising with Kernel Prediction Networks](https://mlsanity.com/paper/1712.02327/) (2017)
- [Handheld Mobile Photography in Very Low Light](https://mlsanity.com/paper/1910.11336/) (2019)
- [Removing Camera Shake via Weighted Fourier Burst Accumulation](https://mlsanity.com/paper/1505.02731/) (2015)

## Built on by (most cited first, 8 of 8)

- [High Dynamic Range and Super-Resolution from Raw Image Bursts](https://mlsanity.com/paper/2207.14671/) (2022)
- [NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results](https://mlsanity.com/paper/2505.12089/) (2025)
- [Joint Multi-Scale Tone Mapping and Denoising for HDR Image Enhancement](https://mlsanity.com/paper/2303.09071/) (2023)
- [Enabling High-Quality In-the-Wild Imaging from Severely Aberrated Metalens Bursts](https://mlsanity.com/paper/2510.10083/) (2025)
- [Deepfakes: we need to re-think the concept of "real" images](https://mlsanity.com/paper/2509.21864/) (2025)
- [Self-Diffusion Driven Blind Imaging](https://mlsanity.com/paper/2510.27439/) (2025)
- [Burst Image Quality Assessment: A New Benchmark and Unified Framework for Multiple Downstream Tasks](https://mlsanity.com/paper/2511.07958/) (2025)
- [Real-Time Scene-Adaptive Tone Mapping for High-Dynamic Range Object Detection](https://mlsanity.com/paper/2608.30400/) (2026)

## Abstract (arXiv)

HDR+ is an image processing pipeline presented by Google in 2016. At its core lies a denoising algorithm that uses a burst of raw images to produce a single higher quality image. Since it is designed as a versatile solution for smartphone cameras, it does not necessarily aim for the maximization of standard denoising metrics, but rather for the production of natural, visually pleasing images. In this article, we specifically discuss and analyze the HDR+ burst denoising algorithm architecture and the impact of its various parameters. With this publication, we provide an open source Python implementation of the algorithm, along with an interactive demo.