Blind Deblurring of Barcodes via Kullback-Leibler Divergence
Gabriel Rioux, Christopher Scarvelis, Rustum Choksi, Tim Hoheisel, Pierre Maréchal · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2019
Barcode encoding schemes impose symbolic constraints which fix certain segments of the image. We present, implement, and assess a method for blind deblurring and denoising based entirely on Kullback-Leibler divergence. The method is designed to incorporate and exploit the full strength of barcode symbologies. Via both standard barcode reading software and smartphone apps, we demonstrate the remarkable ability of our method to blindly recover simulated images of highly blurred and noisy barcodes. As proof of concept, we present one application on a real-life out of focus camera image.