| Greyscale based learning in BPNN for image restoration problem |
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| 作者姓名： | UMAR Farooq 闫雪梅 SADIA Murawwat MUHAMMAD Imran |
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| 作者单位： | School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China;School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China;School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China;School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China |
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| 基金项目： | Supported by the National Natural Science Foundation of China (60772066); Higher Education Commission, Pakistan |
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| 摘 要： | A new method of back propagation learning with respect to the problem of image restoration which is named as greyscale based learning in back propagation neural networks ( BPNN) is investigated. It is observed that by using this method the value of mean square error ( MSE) decreases significantly. In addition,this method also gives good visual results when it is applied in image restoration problem. This method is also useful to tackle the inherited drawback of falling into local minima by reducing its effect on overall system by bifurcating the learning locally different for different grey scale values. The performance of this algorithm has been studied in detail with different combinations of weights. In short,this algorithm provides much better results especially when compared with the simple back propagation algorithm with any further enhancements and without going for hybrid solutions.
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| 关 键 词： | greyscale based learning back propagation neural network( BPNN) image restoration |
| 收稿时间： | 2012/1/10 0:00:00 |
Greyscale based learning in BPNN for image restoration problem |
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| UMAR Farooq,YAN Xue-mei,SADIA Murawwat and MUHAMMAD Imran.Greyscale based learning in BPNN for image restoration problem[J].Journal of Beijing Institute of Technology,2013,22(1):94-100. |
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| Authors: | UMAR Farooq YAN Xue-mei SADIA Murawwat and MUHAMMAD Imran |
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| Affiliation: | School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China |
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| Abstract: | A new method of back propagation learning with respect to the problem of image restoration which is named as greyscale based learning in back propagation neural networks (BPNN) is investigated. It is observed that by using this method the value of mean square error (MSE) decreases significantly. In addition, this method also gives good visual results when it is applied in image restoration problem. This method is also useful to tackle the inherited drawback of falling into local minima by reducing its effect on overall system by bifurcating the learning locally different for different grey scale values. The performance of this algorithm has been studied in detail with different combinations of weights. In short, this algorithm provides much better results especially when compared with the simple back propagation algorithm with any further enhancements and without going for hybrid solutions. |
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| Keywords: | greyscale based learning back propagation neural network(BPNN) image restoration |
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