A Regularization Approach to Blind Deblurring and Denoising of QR Barcodes

10/23/2014
by   Yves van Gennip, et al.
0

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.

READ FULL TEXT

page 8

page 9

page 10

page 11

research
01/22/2022

Blind Image Deblurring: a Review

This is a review on blind image deblurring. First, we formulate the blin...
research
06/22/2021

Polycyclic Codes Associated with Trinomials: Good Codes and Open Questions

Polycyclic codes are a generalization of cyclic and constacyclic codes. ...
research
12/07/2020

Noise2Kernel: Adaptive Self-Supervised Blind Denoising using a Dilated Convolutional Kernel Architecture

With the advent of recent advances in unsupervised learning, efficient t...
research
05/23/2021

FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise

We consider the challenging blind denoising problem for Poisson-Gaussian...
research
04/05/2022

Zero-shot Blind Image Denoising via Implicit Neural Representations

Recent denoising algorithms based on the "blind-spot" strategy show impr...
research
03/29/2023

Exploring Asymmetric Tunable Blind-Spots for Self-supervised Denoising in Real-World Scenarios

Self-supervised denoising has attracted widespread attention due to its ...
research
12/05/2022

Domino Denoise: An Accurate Blind Zero-Shot Denoiser using Domino Tilings

Because noise can interfere with downstream analysis, image denoising ha...

Please sign up or login with your details

Forgot password? Click here to reset