A Regularization Approach to Blind Deblurring and Denoising of QR Barcodes

Using only regularization-based methods, we provide an ansatz-free algorithm for blind deblurring of QR bar codes in the presence of noise. The algorithm exploits the fact that QR bar codes are prototypical images for which part of the image is a priori known (finder patterns). The method has four steps: (i) denoising of the entire image via a suitably weighted TV flow; (ii) using a priori knowledge of one of the finder corners to apply a higher-order smooth regularization to estimate the unknown point spread function (PSF) associated with the blurring; (iii) applying an appropriately regularized deconvolution using the PSF of step (ii); (iv) thresholding the output. We assess our methods via the open source bar code reader software ZBar.