Computer Science > Computer Vision and Pattern Recognition
[Submitted on 23 Oct 2014 (v1), last revised 24 Mar 2017 (this version, v3)]
Title:A Regularization Approach to Blind Deblurring and Denoising of QR Barcodes
View PDFAbstract:QR bar codes are prototypical images for which part of the image is a priori known (required patterns). Open source bar code readers, such as ZBar, are readily available. We exploit both these facts to provide and assess purely regularization-based methods for blind deblurring of QR bar codes in the presence of noise.
Submission history
From: Yves van Gennip [view email][v1] Thu, 23 Oct 2014 12:04:31 UTC (816 KB)
[v2] Mon, 13 Apr 2015 11:10:32 UTC (1,109 KB)
[v3] Fri, 24 Mar 2017 14:51:09 UTC (1,109 KB)
Current browse context:
cs.CV
References & Citations
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.