IPOL Journal · Image Processing On Line
- published
- 2021-05-25
- reference
- Antoine Monod, Julie Delon, and Thomas Veit, An Analysis and Implementation of the HDR+ Burst Denoising Method, Image Processing On Line, 11 (2021), pp. 142–169. https://doi.org/10.5201/ipol.2021.336
Communicated by Jose-Luis Lisani
Demo edited by Jose-Luis Lisani
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.
Download
- full text manuscript: PDF low-res. (1.2MB) PDF (41.2MB) [?]
- source code: TAR/GZ
Supplementary Materials
- Burst of 10 RAW images in .dng format to test the provided codes test data.zip
History
- Note from the editor: the manuscript was updated on June 3, 2021 to add a link to the GitHub repository of the source code. The original version of the paper is available from here.
- Note from the editor: the manuscript of the article was modified on 2022-01-01 to include information about its editors. The original version of the manuscript is available here.
