Published in Image Processing On Line on 2021–05–25. Submitted on 2021–03–01, accepted on 2021–05–05. ISSN 2105–1232 c 2021 IPOL & the authors CC–BY–NC–SA This article is available online with supplementary materials, software, datasets and online demo at https://doi.org/10.5201/ipol.2021.336 2015/06/16 v0.5.1 IPOL article class An Analysis and Implementation of the HDR+ Burst Denoising Method Antoine Monod1,2, Julie Delon1, Thomas Veit2 1 MAP5, Universit´ede Paris, France ({antoine.monod, julie.delon}@u-paris.fr) 2 GoPro Technology, France (
[email protected]) Abstract 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. Source Code The Python implementation of HDR+ has been peer-reviewed and accepted by IPOL. The source code, its documentation, and the online demo are available from the web page of this article1. Compilation and usage instructions are included in the README.txt file of the archive. The code is also available on GitHub2. Keywords: burst denoising; computational photography; motion estimation; temporal filter- ing; high dynamic range; image signal processor 1 Introduction Image noise is a common issue in digital photography, and that issue is even more prominent in smaller sensors used in devices like smartphones or action cameras.