Super-resolving barcode images with an edge-preserving variational Bayesian framework

Limited resolution, blurring, warping, and additive noise associated with image acquisition and storage often make the barcode image degraded, generating low-resolution (LR) barcode images. Barcodes in degraded LR images are difficult to recognize. The goal of this paper is to introduce the potential of super-resolution (SR) technique in conquering the aforementioned challenges, and a variational Bayesian SR method is proposed in this work. Different from the previous work, here, the high-resolution barcode image is estimated through its corresponding posterior probability distribution. The barcode image is made of some homogeneous regions separated by sharp edges, and sometimes it is anisotropic. A universal prior probability distribution was proposed for the barcode image by considering these characteristics. Mathematically, the efficiency of this prior distribution is demonstrated, which can preserve sharp edges and suppress artifacts in the reconstructed barcode images. Moreover, by using the variational Bayesian framework, the motion parameters and hyperparameters can be estimated accurately and efficiently, ensuring the success of the SR technique. In order to overcome the difficulty caused by nonlinearity, the Taylor expansion method is introduced to solve the proposed SR problem. Eventually, the simulated and real data experiments show the encouraging performance of the proposed SR method. It increases certainly the reconstruction quality, and could be considerably robust against blur and noise. It is believed that the variational SR technique in the barcode auto-identification technique should open a further perspective of coping with technology challenge. (C) 2016 SPIE and IS&T