Name
Abstract | ||
|---|---|---|
Various image preprocessing applications for two dimensional (2D) barcode involve reversing the degradation operations (e.g. deblurring). Most of the previously proposed deblurring approaches focus on the construction of suitable deconvolution models, which have shown significant performance at laboratory level. However, the model-based image deblurring solutions might not work well in practical scenarios. To deal with this problem, we propose a convolutional neural network (CNN) based framework to tackle the parameter-free situation for 2D barcode deblurring. The proposed solution leverages the deep learning technique to bridge the gap between traditional model-based methods and requirement of reversing the blurry 2D barcode images. Experiments on practically blurred quick response (QR) barcode images demonstrate that the proposed approach achieves the superior performance in comparison with state-of-the-art model-based image deblurring approaches. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11042-018-5802-2 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Image Processing, Deblurring, Convolutional Neural Network, 2D barcode | Computer vision,Pattern recognition,Deblurring,Convolutional neural network,Computer science,Reversing,Deconvolution,Image processing,Preprocessor,Artificial intelligence,Deep learning,Barcode | Journal |
Volume | Issue | ISSN |
78 | 1 | 1573-7721 |
Citations | PageRank | References |
1 | 0.38 | 23 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haitao Pu | 1 | 1 | 1.73 |
| Mingqu Fan | 2 | 5 | 2.50 |
| Jinliang Yang | 3 | 3 | 1.54 |
| Jian Lian | 4 | 37 | 11.49 |