# A Regularization Approach to Blind Deblurring and Denoising of QR Barcodes

arXiv: https://arxiv.org/abs/1410.6333 · HTML: https://mlsanity.com/paper/1410.6333/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2014.

## Built on by (most cited first, 3 of 3)

- [Neuromorphic Synergy for Video Binarization](https://mlsanity.com/paper/2402.12644/) (2024)
- [An Adaptive Edge-Guided Dual-Network Framework for Fast QR Code Motion Deblurring](https://mlsanity.com/paper/2510.12098/) (2025)
- [Blind Deconvolution of Binary and Pattern Images with Pixel Intensity Constraints and Sparse Gradient Prior](https://mlsanity.com/paper/2609.23268/) (2026)

## Abstract (arXiv)

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.