搜索资源列表
deconv
- 采用matlab编写的数字图像恢复程序，和数字图像解卷积操作的程序。对常用的几种解卷积方法都有例子。-using Matlab prepared by the digital image recovery procedures, and digital image deconvolution operating procedures. Several pairs of common deconvolution method has examples.
2006121819221395587
- MATLAB数字图象处理的主要源码.包括图象去噪变换增强恢复识别压缩的各种算法-MATLAB digital image processing the main source. Transform Image Denoising including enhanced recovery recognition of the compression algorithms
motion-blur-Image-Restoration
- 用matlab开发的运动模糊复原程序，包括运动参数的估计、维纳滤波器的源代码-Development of the motion blur with matlab recovery procedures, including the motion parameters estimation, Wiener filter source code
wavelets
- 基于小波变换的图像压缩代码，包括压缩比、运行时间和计算复原图像的峰值信噪比-Image compression based on wavelet transform code, including compression ratio, operation time and recovery images of the peak signal to noise ratio calculation
segmeeeeeeeeeeeeeee.tar
- A general technique for the recovery of signi cant image features is presented. The technique is based on the mean shift algorithm, a simple nonparametric pro- cedure for estimating density gradients. Drawbacks of the current methods (includi
ImageRecovery.tar
- Simple Image Recovery method
2009_the_Algorithm_Improves_in_Dyadic_System_Owing
- 嵌入式系統中SPIHT算法改進的研究: 本文在重點分析研究SPIHT算法的基礎上,結合現有技術提出一種改進的SPIHT算法.改進的SPIHT算法通過采用提升小波變換、結合人眼視覺特性、對算法的分集排序過程進行優化等措施,進一步提高壓縮性能,而且圖像復原質量較好.-SPIHT algorithm for embedded systems to improve the research: In this paper, the focus of analysis in the SPIHT algori
l1magic-1.1
- 实现对图像的分块并进行压缩感知算法的恢复-Achieve the right image sub-block and recovery of compressed sensing algorithm
recovery
- 最小方差恢复算法，用于恢复图像中未知位置的点像素-Minimum variance recovery algorithm, used to restore the image point of the unknown location of the pixel
Mixedthreshold_Image-Recovery
- 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 comp
Compressive_gi
- We describe an advanced image reconstruction algorithm for pseudothermal ghost imaging, reducing the number of measurements required for image recovery by an order of magnitude. The algorithm is based on compressed sensing, a technique that enables t
BP
- 压缩传感理论是今年来信号和图像处理领域的热点，这是一个matlab编写的基于BP算法的压缩传感程序，可以模拟压缩传感理论的采样和图像恢复过程。-Compressed sensing theory is that this year the field of signal and image processing hotspot, this is a matlab prepared BP algorithm based on compressed sensing program that can
OMP
- 压缩传感理论是今年来信号和图像处理领域的热点，这是一个matlab编写的基于OMP算法的压缩传感程序，可以模拟压缩传感理论的采样和图像恢复过程。-Compressed sensing theory is that this year the field of signal and image processing hotspot, this is a matlab prepared OMP algorithm based on compressed sensing program that ca
StOMP
- 压缩传感理论是今年来信号和图像处理领域的热点，这是一个matlab编写的基于stOMP算法的压缩传感程序，可以模拟压缩传感理论的采样和图像恢复过程。-Compressed sensing theory is that this year the field of signal and image processing hotspot, this is a matlab prepared stOMP algorithm for compressed sensing program that can
image-CS
- 基于CS的图像压缩解压，包括CoSaMP和OMP恢复算法，离散小波变换矩阵构造，具体对图像进行压缩解压-CS-based image compression and decompression, including CoSaMP and OMP recovery algorithms, discrete wavelet transform matrix structure, specifically for image compression and decompression
l1-norm-recovery
- the recovery of the 2D SAR image with l1-norm minimization
CPA-to-restore-image
- 用统计中的主成分分析思想对图像进行压缩，再用前几个主成分实现对程序的恢复。-Statistical analysis of the main components of thinking for image compression, and then the first few principal components to achieve the program s recovery.
Image-Retrieval
- 图像检索与恢复程序。可以用于简单的图像检索问题，识别验证码等-Image Retri and recovery procedures. It can be used for simple image retri , identification verification codes
CS-recovery-LevelSet-Normals
- 压缩感知恢复算法，使用新的范数来提升图像恢复能力，包含论文和代码。-We propose a compressive sensing algorithm that exploits geometric properties of images to recover images of high quality few measurements. The image reconstruction is done by iterating the two following steps: 1) e
LLRGTV
- Hyperspectral Image Denoising Using Local Low Rank Matrix Recovery and Global Spatial Spectral Total Variation