搜索资源列表
GMC_software
- 用于稀疏优化的最新非凸函数GMC算法，可用于信号处理以及图像处理。(The latest non convex function GMC algorithm for sparse optimization, can be used for signal processing and image processing.)
KSVD
- 这是KSVD稀疏分解算法，，可以很好的用于图像的去噪(This is a KSVD sparse decomposition algorithm, which can be used for image denoising)
ksvd-omp
- 实现稀疏表示算法，以lena图像为例，主要用ksvd和omp算法(The sparse representation algorithm is implemented. The Lena image is taken as an example, mainly using the ksvd and OMP algorithms.)
压缩采样匹配追踪重构算法
- 压缩感知稀疏度自适应重构算法，含有子功能程序和主程序，注意程序中数值与所选图像尺寸的匹配(Compressed sensing sparse adaptive reconstruction algorithm, contains subfunction program and main program, notice the value of the program matching the selected image size.)
S_R
- 稀疏字典,图像处理，k-svd, omp(Sparse representation)
bcs-spl-1.5-1 (1).tar
- BCS-SPL将图像的基于块的压缩感测采样（BCS）与平滑的投影Landweber（SPL）迭代重建相结合。采样是通过逐块应用随机矩阵来驱动的，而重建则是预期的Landweber（PL）重建（也称为迭代硬阈值（IHT））的变体，其结合平滑操作（维纳滤波）减少块效应。实质上，除了PL所固有的稀疏性之外，这种滤波操作还能提供平滑性。(BCS-SPL combines block-based compressed-sensing sampling (BCS) of an image with a sm
Fisher字典学习
- 基于稀疏表示的高光谱图像分类的Fisher字典学习方法matlab代码(Hypersynthetic image classification based on sparse representation in Fisher dictionary learning matlab code.)
Indian
- 使用基于词典的稀疏表示高光谱图像分类,多任务联合稀疏表示和逐步MRF优化的高光谱图像分类(Dictionary-based sparse representation hyperspectral image classification, multi-task joint sparse representation and stepwise MRF optimized hyperspectral image classification)
DeepLearningforPassiveSyntheticApertureRadar
- 通过大量的数值模拟，我们的深度学习为基础的方法优于传统的稀疏编码方法在计算方面和重建图像质量，特别是当没有对发射机的信息是可用的。(our deep learning based method is better than the traditional method in calculation and the quality of image reconstruction, especially when no information is available on the transmit
MLRI
- 利用最小化拉普拉斯残差插值算法处理拜耳图像，采用稀疏拉普拉斯滤波器处理红绿蓝三种像素，达到良好的插值效果。(Using the minimized Laplasse residual interpolation algorithm to deal with Bayer image, three pixels of red and green blue are processed by sparse Laplasse filter to achieve good interpolation eff
multi-kernel-path-1.1
- 多核变换，稀疏变换，核变换自适应学习，图形图像变换(Descr iption ----------- Multi-Kernel-Path package is a Matlab program that computes the entire regularization path for multiple-kernel learning problems. More precisely, it solves any learning problem with a smooth loss
L0smoothing
- 图像平滑，L0范数，稀疏，锐化，滤波 通过限制非零梯度的数量来实现最高对比度边缘，同时以全局方式实现平滑(Image Smoothing L0 sparsity highest-contrast edges by confining the number of nonzero gradients, while smoothing is achieved in a global manner)
ScSR
- 杨建超的将稀疏表达用于图像超分辨率重建的文章赋代码(Example matlab code for the algorithm proposed in "Image super-resolution via sparse representation" TIP 2010.)
2
- 基于稀疏分解的形态学成分分析，在分解图像的同时完成了去噪任务。(Based on the morphological component analysis of sparse decomposition, the image is decomposed and the denoising task is completed at the same time.)
ksvd算法
- 图像去噪稀疏表示，新型的去噪方法相比传统的方法有很多优点。