搜索资源列表
Simulation-visual-mechanism
- 提出一个小波域多尺度马尔柯夫随机场模型用于模拟视觉系统在图像分割中的若干功能。针对人类视觉系统具有特征检测器、等级层次性、双向连续性、学习机制等功能，对输入场景，该模型用小波变换提供该场景图像的稀疏表示，模拟特征检测器功能 用金字塔结构模拟等级层次性 用两类信息流模拟双向连接性，分别刻画自底向上的输入图像特征提取过程以及自顶向下的反馈过程 用迭代过程模拟学习机制 采用多尺度马尔柯夫随机场模型实现图像分割。-Put forward a wavelet domain multi-scale mark
paper3
- 基于小波稀疏表示的压缩感知SAR 成像算法研究.pdf-A Compressive Sensing Imaging Approach Based on Wavelet Sparse Representation
Fast-Discrete-Curvelet-Transforms
- 快速曲线波变换理论属于稀疏表达的范畴，采用基函数与信号的内积形式实现信号的稀疏表示。-An Alogrithm for Multimodal Biometric Recognition Based on Feature Level and the Second-Generation Curvelet Transform
Tetrolet_Transform
- Tetrolet变换的原代码，一种稀疏表示的小波变换，由haar变换改进得到-Tetrolet transform the original code, a sparse representation of the wavelet transform, haar transform improved
WT-OMPmatrix
- 对图像进行压缩感知，通过构造小波正交变换矩阵进行稀疏表示，用OMP重构-CS of image based on WT
CS_Primary_tutorial
- CS压缩传感的初级教学代码，使用OMP重构，已注释，包括1维信号，2维图像的重构，分别使用dct和小波稀疏，列扫描和分块法进行omp重构-CS compressed sensing primary teaching code using OMP remodeling, already commented, including a 1-dimensional signals, 2-dimensional image reconstruction, respectively, using the D
Wavelet_IRLS
- 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为ILRS算法，对256*256的lena图处理，比较原图和IRLS算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间-Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix
Wavelet_OMP
- 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为OMP算法，对256*256的lena图处理，比较原图和OMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间 -Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix
Wavelet_SP
- 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为SP算法，对256*256的lena图处理，比较原图和SP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间-Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and
Wavelet_ROMP
- 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为ROMP算法，对256*256的lena图处理，比较原图和ROMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间 -Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matr
Wavelet_SL0
- 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为SL0算法，对256*256的lena图处理，比较原图和SL0算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间 -Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix
ShearLab-PPFT-1.0
- 图形处理中需要用到的剪切小波变换。可以稀疏表示信号。-Graphics processing needed shear wavelet transform. Sparse representation of the signal.
Wavelet_Sparse
- Matlab小波稀疏代码。本程序实现小波在矩阵方程求解中的应用。-Matlab wavelet sparse code. This procedure achieved wavelet matrix equation solver.
cs
- 压缩感知，稀疏基为小波基，omp，典型的压缩感知代码-failed to translate
goodbook
- 里面介绍了很多小波分析、稀疏表示、盲源分离方面的书籍-Which introduced a lot of wavelet analysis, sparse representation and blind source separation of books
inpaint
- 基于压缩感知的图像修复,，基于图像在复数小波变换上的稀疏性，利用迭代硬阈值方法 求解重构模型，进而获得重构图像.-Based on compressed sensing image restoration, image-based complex wavelet transform on the sparsity of the iterative method for solving hard threshold reconstruction model, and then get the
Toolbox_sparseMRI_PBDW
- 基于分块小波的图像框架的核磁共振图像稀疏重建算法工具箱。-Ts image smoothing. Keep the big picture edges smoothed images of small edges. Has the effect of drawing cartoon images.
Wavelet-OMP
- 基于小波变换和OMP算法的稀疏表示,不仅包括适用于黑白图片的程序，也包括适用于彩色图片的程序。-the sparse representation basing on wavelet and omp,not only for the gray image,but also color image
Curvelet
- 曲波变换是图像处理领域中稀疏表示最常用的一种字典，其中MCA分解模型中经常用到。-Bo transform the field of image processing is the most common kind of sparse representation dictionary MCA decomposition model which is often used.
compressed-sensing
- 压缩感知，稀疏表示采用小波基表示，压缩测量采用随机高斯矩阵，重构算法是omp重构-Compressed sensing, sparse representation using wavelet representation, compression measurements using random Gaussian matrix remodeling reconstruction algorithm is omp