搜索资源列表
figure64
- 稀疏分解图像重建程序，把图像分解成多个小块图像，然后再各个子块重建后边缘处理后合并成整个图像。-sparse decomposition image reconstruction process, the image is divided into a number of small images, then each sub-block redevelopment edge after the merger into the whole image.
low-rank-ksvd
- 低秩的求解 denoise an image by sparsely representing each block with the already overcomplete trained Dictionary, and averaging the represented parts. Detailed descr iption can be found in "Image Denoising Via Sparse and Redundant representations
block_mp
- 压缩感知里面的稀疏重建问题，在原来MP的算法的基础上，提出了分块的思想，即将原子库分块后，测试算法的性能。-Compressive sensing is refered as the block sparse algorithm is described,that is based on the original MP algorithm,and the atom libruary is divided into blocks ,and the performance is testified.
bcs-spl-1.3-1.tar
- 分块压缩感知中稀疏表示增加方向性提高重构质量-Block compression to increase directional to improve the reconstruction quality perception in sparse
BCS
- 压缩传感是一个从2006年左右开始兴起的研究领域，它关注于如何采样信号，也就是信号的采样方式或者压缩方式。通过设计一种特殊的采样方案，可以使得采样频率降低为信号的“信息率”，而不是传统的奈奎斯特采样率，于是，实际的采样率可以大大低于奈奎斯特频率，却只损失很少的信息量，依然保持了充足的信息量足以恢复出采样前的原始信号。这个研究思想挑战了奈奎斯特频率的理论极限，会对整个信号处理领域产生极其深远的影响，同时，信号处理的许多应用领域也会随之发生根本性的发展和变化。 -Compressive sens
Adaptive-Block
- 针对图像稀疏分解的计算时间复杂度非常高这个问题,提出了分块自适应图像稀疏分解算 法。该算法根据稀疏分解计算时间复杂度和待分解图像大小之间的关系,把待分解图像分成互不重 叠的小块,然后对每个小块图像进行稀疏分解。根据每一块的复杂程度,自适应地决定稀疏分解的 结束。-Sparse decomposition of image is very high computational complexity of this problem, a block adaptive image spar
SensingMatrixDesign
- 利用块稀疏的方法求解感知矩阵，在压缩感知、图像恢复方面都可以应用-Method for solving block sparse perception matrix in compressed sensing, image restoration can be applied
DCT_Gaosi_fenkuai
- 对256*256大小的8bit灰度lena图像进行仿真 将图像分为16*16的分块进行计算 稀疏矩阵采用DCT矩阵，观测矩阵采用高斯随机矩阵，重构采用OMP算法- 256* 256 size lena image simulation 8bit grayscale image is divided into 16 * 16 calculate block sparse matrix using DCT matrix, observation matrix using Ga
fast_sc1
- 基于patch块的稀疏编码的matlab代码的实现，涉及到基和系数最小化的优化问题-Based on patch block sparse coding matlab code to achieve that relates to the base and the coefficient minimization optimization problem
block-sparse
- 压缩感知matlab代码及说明，适合初学者学习使用,关于分块稀疏-Compressed sensing matlab code and instructions for beginners to learn to use, on the block sparse
block sparse
- block sparse资料，可以对结构稀疏学习有帮助
train_coupled_dict
- 用于对所获取的图像块做联合字典的稀疏表示训练-For the acquired image block sparse representation of the dictionary to do joint training
FISTA-block-sparse
- FISTA block sparse 压缩感知加速恢复算法-FISTA block sparse
BSBL-FM
- 快速边缘的块稀疏贝叶斯学习代码,BSBL-FM,用以对穿墙雷达块目标成像-Fast marginalized block sparse Bayesian learning codes
BSBL-FM-master
- 基于贝叶斯学习方法的块稀疏信号压缩感知算法-A fast implementation of the Block Sparse Bayesian Learning algorithm
l1_ls_matlab
- Block magic for recovery sparsity.
GISA
- Block GISA for recovery sparsity.
BSBL_public
- 这是一篇论文中实验部分的源代码，希望对学习压缩感知的同学有帮助(The experimental part of the source code,Article title is Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning.)
block_mimo_ ofdm_channelestimation
- 块稀疏 mimo 方位估计算法。mimo系统。块稀疏。(Block sparse MIMO azimuth estimation algorithm. MIMO system. The block is sparse.)
MSBL_code
- 块稀疏贝叶斯方法，使用块稀疏贝叶斯求解稀疏信号(Block Sparse Bayesian Method)