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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dridi, N. Delignon, Y. Sawaya, W. Garnier, C. |
| Copyright Year | 2011 |
| Description | Author affiliation: Université Lille Nord de France, Institut TELECOM, TELECOM Lille1, LAGIS FRE 3303 CNRS, France (Dridi, N.; Delignon, Y.; Sawaya, W.; Garnier, C.) |
| Abstract | Decoding a severely blurred 2D barcode can be considered as a special case of blind image restoration issue. In this paper, we propose an appropriate system model which includes the original image with the particularities related to barcode, the blur and the observed image. We develop an unsupervised algorithm that jointly estimates the blur and detects the symbols using the maximum likelihood (ML) criterion. Besides, we show that when taking into account the spatial properties of the barcode, the prohibitive complexity of the ML algorithm can be reduced without degrading its performance. Simulation results show that the algorithm performs accurate estimation of the blur and achieves good performance for symbol detection which is close to that obtained with supervised algorithm. |
| Starting Page | 32 |
| Ending Page | 36 |
| File Size | 390212 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781457708411 |
| ISSN | 18455921 |
| e-ISBN | 9789531841597 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-09-04 |
| Publisher Place | Croatia |
| Access Restriction | Subscribed |
| Subject Keyword | Signal processing algorithms Channel estimation Signal processing Estimation Hidden Markov models Complexity theory Mathematical model |
| Content Type | Text |
| Resource Type | Article |
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