Contact Project Developer Ashish D. Tiwari [astiwz@gmail.com]
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C#.NET ASP.NET Image Processing BE-Engineering(CO/IT) ME-Engineering(CO/IT) BCS MCS BCA MCA MCM BSC Computer/IT MSC Computer/IT Diploma (CO/IT) IEEE-2015

A Regularization Approach to Blind Deblurring And Denoising of QR Barcodes

Blind Deblurring And Denoising of QR Barcodes
Abstract-Synopsis-Documentation

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.

EXISTING SYSTEM:

We note that there are currently a wealth of regularizationbased methods for deblurring of general images. For a signal f , many attempt to minimiz .Whenever a user types in her password in a bank’s signin box, the keylogger intercepts the password. The threat of such keyloggers is pervasive and can be present both in personal computers and public kiosks; there are always cases where it is necessary to perform financial transactions using a public computer although the biggest concern is that a user’s password is likely to be stolen in these computers. Even worse, keyloggers, often rootkitted, are hard to detect since they will not show up in the task manager process list.

PROPOSED SYSTEM:

Proposes an iterative Increment Constrained Least Squares filter method for certain 2D matrix bar codes within a Gaussian blurring ersatz. In particular, they use the L-shaped finder pattern of their codes to estimate the standard deviation of the Gaussian PSF, and then restore the image by successively implementing a bi-level constraint  ,Our approach to solving the problem is to introduce an intermediate device that bridges a human user and a terminal. Then, instead of the user directly invoking the regular authentication protocol, she invokes a more sophisticated but user-friendly protocol via the intermediate helping device. Every interaction between the user and an intermediate helping device is visualized using a Quick Response (QR) code. The goal is to keep user-experience the same as in legacy authentication methods as much as possible, while preventing keylogging attacks.

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