二维码（扫一下试试看！） | | 基于图像熵分块的压缩感知字典学习算法 | | Dictionary Learning Algorithm for Compressed-Sensing Based on the Entropy of Image Patches | | 投稿时间：2018-03-11 | | DOI：10.15918/j.tbit1001-0645.2019.05.014 | | 中文关键词:灰度梯度矩阵图像熵稀疏字典奇异值分解 | | English Keywords:gray-gradient co-occurrence matriximage entropysparse dictionarysingular value decomposition | | 基金项目:国家自然科学基金资助项目（61471412，61771020，61373262） | | | 摘要点击次数:838 | | 全文下载次数:380 | | 中文摘要: | | 针对传统字典学习算法预处理阶段未考虑图像内外部特征的问题，提出一种基于灰度梯度矩阵的图像熵字典学习算法.该算法通过灰度梯度矩阵计算图像块熵值，并对各图像块进行分类，每类数据组合成训练数据集，再利用基于系数矩阵的奇异值分解算法更新各类子字典.对测试图像的稀疏表示系数进行重建实验，仿真结果表明，该算法可高效训练出自适应稀疏字典，显著提高图像重建精度. | | English Summary: | | The traditional dictionary learning algorithm doesn't take the internal and external characteristics of the image into account in pre-process. A novel algorithm about image entropy was proposed based on gray-gradient co-occurrence matrix solution. Calculating the entropy of image patches by gray-gradient co-occurrence matrix, each image patch was classified and each type of patches was combined into training set. All kinds of sub-dictionaries were updated by singular value decomposition algorithm based on coefficient matrix. The reconstruction experiment was carried out based on the sparse representation coefficient of the test image. The simulation results show that the algorithm can effectively produce adapted sparse dictionary and significantly improve the accuracy of the reconstruction of image. | | 查看全文查看/发表评论下载PDF阅读器 | |
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