针对磁共振(magnetic resonance,MR)幅度图像中带有不易去除的与信号相关的莱斯(Rician)噪声问题,利用其复数图像中的实部与虚部所含噪声为不相关的加性高斯白噪声这一特性,代替对幅度图像直接去噪,提出将原始对偶字典学习(predual dict...针对磁共振(magnetic resonance,MR)幅度图像中带有不易去除的与信号相关的莱斯(Rician)噪声问题,利用其复数图像中的实部与虚部所含噪声为不相关的加性高斯白噪声这一特性,代替对幅度图像直接去噪,提出将原始对偶字典学习(predual dictionary learning,PDL)算法用于对MR复数图像的实部与虚部分别进行去噪,然后组合得到幅度图像的方法.经仿真实验和在HT-MRSI50-50(50 mm)1.2 T小动物核磁共振系统中的实际应用,证明所提方法较直接对幅度图像去噪取得更好的效果,在有效去除MR图像噪声的同时能较好地保持图像中的细节.与经典的字典学习算法核奇异值分解(kernel singular value decomposition,K-SVD)相比,PDL算法去噪效果优于K-SVD算法,而运算速度提高约5倍.与经典的基于非局部相似块的三维块匹配滤波(block-matching and 3D filtering,BM3D)算法相比,在噪声水平较低时PDL算法略优于BM3D算法,噪声水平较高时BM3D算法略优于PDL算法,两者总体比较接近.展开更多
For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the i...For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the input data of the model.Next,the dictionary learning techniques using the K-mean singular value decomposition(K-SVD)algorithm and the orthogonal matching pursuit(OMP)algorithm are used to obtain the corresponding sparse encoding based on all the input data,i.e.the initial dictionary.Then,to build the global prediction model,the sparse coding vectors are used as the input of the model of the kernel extreme learning machine(KELM).Finally,to verify the effectiveness of the combined K-SVD-OMP and KELM method,the proposed method is applied to a instance of the photovoltaic power prediction.Compared with KELM,SVM and ELM under the same conditions,experimental results show that different combined sparse representation methods achieve better prediction results,among which the combined K-SVD-OMP and KELM method shows better prediction results and modeling accuracy.展开更多
文摘针对磁共振(magnetic resonance,MR)幅度图像中带有不易去除的与信号相关的莱斯(Rician)噪声问题,利用其复数图像中的实部与虚部所含噪声为不相关的加性高斯白噪声这一特性,代替对幅度图像直接去噪,提出将原始对偶字典学习(predual dictionary learning,PDL)算法用于对MR复数图像的实部与虚部分别进行去噪,然后组合得到幅度图像的方法.经仿真实验和在HT-MRSI50-50(50 mm)1.2 T小动物核磁共振系统中的实际应用,证明所提方法较直接对幅度图像去噪取得更好的效果,在有效去除MR图像噪声的同时能较好地保持图像中的细节.与经典的字典学习算法核奇异值分解(kernel singular value decomposition,K-SVD)相比,PDL算法去噪效果优于K-SVD算法,而运算速度提高约5倍.与经典的基于非局部相似块的三维块匹配滤波(block-matching and 3D filtering,BM3D)算法相比,在噪声水平较低时PDL算法略优于BM3D算法,噪声水平较高时BM3D算法略优于PDL算法,两者总体比较接近.
基金National Natural Science Foundation of China(No.51467008)。
文摘For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the input data of the model.Next,the dictionary learning techniques using the K-mean singular value decomposition(K-SVD)algorithm and the orthogonal matching pursuit(OMP)algorithm are used to obtain the corresponding sparse encoding based on all the input data,i.e.the initial dictionary.Then,to build the global prediction model,the sparse coding vectors are used as the input of the model of the kernel extreme learning machine(KELM).Finally,to verify the effectiveness of the combined K-SVD-OMP and KELM method,the proposed method is applied to a instance of the photovoltaic power prediction.Compared with KELM,SVM and ELM under the same conditions,experimental results show that different combined sparse representation methods achieve better prediction results,among which the combined K-SVD-OMP and KELM method shows better prediction results and modeling accuracy.
