TL;DR
This paper analyzes Google's HDR+ burst denoising algorithm, discussing its architecture and parameters, and provides an open-source Python implementation and interactive demo for improved understanding and reproducibility.
Contribution
It offers a detailed analysis of HDR+ denoising architecture and releases an open-source implementation with an interactive demo, enhancing transparency and accessibility.
Findings
Analysis of HDR+ denoising architecture
Impact assessment of algorithm parameters
Open-source implementation and demo available
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
