Fast blur removal for wearable QR code scanners

We present a fast restoration-recognition algorithm for scanning motion-blurred QR codes on handheld and wearable devices. We blindly estimate the blur from the salient edges of the code in an iterative optimization scheme, alternating between image sharpening, blur estimation, and decoding. The restored image is constrained to exploit the properties of QR codes which ensures fast convergence. The checksum of the code allows early termination when the code is first readable and precludes false positive detections. General blur removal algorithms perform poorly in restoring visual codes and are slow even on high-performance PCs. The proposed algorithm achieves good reconstruction quality on QR codes and outperforms existing methods in terms of speed. We present PC and Android implementations of a complete QR scanner and evaluate the algorithm on synthetic and real test images. Our work indicates a promising step towards enterprise-grade scan performance with wearable devices.