| 带稀疏惩罚的LCNN盲目图像复原 |
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| 引用本文： | 陈科,朱清新,易涛.带稀疏惩罚的LCNN盲目图像复原[J].电子科技大学学报(社会科学版),2008(6). |
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| 作者姓名： | 陈科 朱清新 易涛 |
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| 作者单位： | 电子科技大学计算机科学与工程学院;中国电子集团公司第三十研究所; |
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| 基金项目： | 国家自然科学基金(60671033) |
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| 摘 要： | 为了加强算法的稀疏性和稳定性,在SCAD基础上提出了一种新的稀疏惩罚函数,并加入到拉格朗日约束神经网络中,以克服传统盲源分离方法和独立分量分析方法的缺陷,有效地避免了方程的病态问题,提高盲目图像复原的稀疏性、稳定性和准确性。通过人工数据和真实数据的不同复原算法对比实验,证明了带稀疏惩罚的拉格朗日约束神经网络盲目图像复原技术具有良好的图像复原效果。
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| 关 键 词： | 盲目图像复原 拉格朗日约束神经网络 稀疏惩罚函数 子带分解 |
Blind Image Restoration Using LCNN with Sparse Penalty |
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| CHEN Ke,ZHU Qing-xin, YI Tao.Blind Image Restoration Using LCNN with Sparse Penalty[J].Journal of University of Electronic Science and Technology of China(Social Sciences Edition),2008(6). |
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| Authors: | CHEN Ke ZHU Qing-xin YI Tao |
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| Institution: | CHEN Ke1,ZHU Qing-xin1,, YI Tao2 (1. School of Computer Science , Engineering,University of Electronic Science , Technology of China Chengdu 610054,2. No.30 Institute,China Electronic Technology Corporation Chengdu 610041) |
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| Abstract: | In order to improve sparsity and robustness,a novel sparse penalty function based on smoothly clipped absolute diviation(SCAD) is proposed and applied to Lagrange Constraint Neural Network(LCNN) . This method can solve ill-conditioned problem and improve sparsity,stability,and accuracy in blind image restoration. Both artificial and real-world data are calculated under some different restoration methods. Results of the experiments show that Lagrange constraint neural network with sparse penalty has better r... |
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| Keywords: | blind image restoration Lagrange constraint neural network sparse penalty function subband decomposition |
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