Description: 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. Platform: |
Size: 64113 |
Author:fanghui20006 |
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Description: 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. Platform: |
Size: 63488 |
Author:fanghui20006 |
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Description: sba, a C/C++ package for generic sparse bundle adjustment is almost invariably used as the last step of every feature-based multiple view reconstruction vision algorithm to obtain optimal 3D structure and motion (i.e. camera matrix) parameter estimates. Provided with initial estimates, BA simultaneously refines motion and structure by minimizing the reprojection error between the observed and predicted image points. Platform: |
Size: 393216 |
Author:picab |
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Description: Bundler is a structure-from-motion system for unordered image
collections (for instance, images from the Internet). Bundler takes a
set of images, image features, and image matches as input, and
produces a 3D reconstruction of the camera and (sparse) scene geometry
as output. The system, described in [1] and [2], reconstructs the
scene incrementally, a few images at a time, using a modified version
of the Sparse Bundle Adjustment package of Lourakis and Argyros [3] as
the underlying optimization engine.
Currently, Bundler has been primarily compiled and tested under Linux
(though a Windows version for cygwin has also been released). Platform: |
Size: 5941248 |
Author:陳奕均 |
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Description: 此代码用于完成MRI图像的压缩采样重建。它是论文"Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging", 2007中的源码-This is an implementation of Compressed Sensing reconstruction for MRI data.
It implements the non-linear conjugate sub-gradient algorithm as described in
the paper "Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging", 2007
Magn Res Med, In Press.
Platform: |
Size: 19181568 |
Author:李琳 |
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Description: 使用MP将图像稀疏分解并且精确重构，其中使用2D的GA原子-MP will use the image sparse decomposition and perfect reconstruction, in which the GA using 2D atomic Platform: |
Size: 174080 |
Author:whyuan |
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Description: 附件代码为正交匹配追踪的源程序，用matlab编写，程序简单实用。-Annex code orthogonal matching pursuit of the source, written with matlab, the program is simple and practical. Platform: |
Size: 68608 |
Author:miaoguijun |
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Description: 压缩感知中基追踪重构方法，用于稀疏信号的重构，本程序用于图像重构-Based tracking in compressed sensing reconstruction methods for sparse signal reconstruction, the procedure used for image reconstruction Platform: |
Size: 3072 |
Author:曹离然 |
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Description: 基于压缩感知的图像处理，分别使用二维DCT、FFT和一维dwt变换对图像信号进行稀疏变换，然后使用正交匹配追踪算法进行重构，在进行相应的逆变换-This is a image processing procedure based on compressed sensing which respectively uses two-dimensional DCT, FFT and one-dimensional dwt transform to sparse the image signal and then uses the orthogonal matching pursuit algorithm for reconstruction. Platform: |
Size: 41984 |
Author:刘会影 |
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Description: 代码给出了基于混合门限带迭代步长的稀疏图像重构。特别地，压缩采样矩阵为抽样傅里叶变换矩阵，利用2D-FFT,大大降低了计算复杂度。-The mixedthreshold sparse image reconstruction with step is given in the package. In particular， the 2D-FFT is used to disign the sample matrix, which can reduce the computational compexity dramatically. Platform: |
Size: 28672 |
Author:周清保 |
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Description: 用小波变换作为稀疏基，采取OMP算法将图像重建恢复，由于算法计算量大会导致成像时间过长，程序用改进的分块处理缩短了时间，-Wavelet transform as a sparse base, take OMP algorithms to restore the image reconstruction algorithm to calculate the General Assembly led to the long imaging time, the program using a modified block processing to shorten the time Platform: |
Size: 34816 |
Author:任甜甜 |
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Description: 合稀疏贝叶斯学习(SBL)和可压缩传感理论(CS)，给出一种在噪声测量条件下重建可压缩图像的方法。该方法将cS理论中图像重建过程看作一个线性回归问题，而待重建的图像是该回归模型巾的未知权值参数；利用sBL方法对权值赋予确定的先验条件概率分布用以限制模型的复杂度，并引入超参数-
Hop sparse Bayesian learning ( SBL ) and compressible sensing theory ( CS ) , give a compressible image reconstruction in the noise measurement conditions . The method of the CS theory image reconstruction process as a linear regression problem , the image to be reconstructed is unknown weighting parameters of the regression model towel SBL method to determine the weights given a priori probability distribution to limit the complexity of the model and the introduction of the hyper-parameters Platform: |
Size: 408576 |
Author:lili |
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Search - sparse image reconstruction