搜索资源列表
10Adaptive-multiresolution-analysis-
- 10Adaptive multiresolution analysis structures and shearlet systems ShearletUEP-10Adaptive multiresolution analysis structures and shearlet systems ShearletUEP
shearletnew
- Demo:演示程序（注意图像路径） ShearletCoef: 系数计算程序 mybeta: 伪极坐标 Fourier 变换（文件名是暂时的用法） phi0,psi0,psid: 低高频图像点阵显示（myu,myv用于理论分析，不被 Demo 调用） psiStock: FPsi1(2)_x（有编号）: shearlet 函数类 bump: 钟形光滑函数 ramp: 斜升光滑函数（即旧版中的 smooth） 新版比旧版提高了框架函数的连续性——对
paper
- 有关图像处理用shearlet变换来处理图像融合的例子代码。-About image processing shearlet transform image fusion processing example code.
paper3
- Adaptive multiresolution analysis structures and shearlet systems
paper1
- Sparse directional image representations using the discrete shearlet transform
ShearLab3Dv11
- shearlet变换代码，matlab和c++。-shearlet transform
RICKER
- 多道雷克子波的的合成，和利用shearlet处理雷克子波的方法，效果良好-Synthesis of multi-channel Ricker waves, and methods of treatment utilizing shearlet Ricker wave to good effect
Fourier_based_shearlet_transform
- shearlet变换的应用，shearlet变换对于图像随机噪声的压制，有关于随机噪声的压制-shearlet conversion applications, shearlet transform the image of random noise suppression, repression about random noise
Shear
- shearlet图像融合的源代码,代码的功能是实现对两幅聚焦不同的图像进行分解-Shearlet image fusion of the source code, the function of the code is to achieve the two focus on the different image decomposition
2015-LiYuming-IQA
- LiYuming2015年在Neurocomputing发表的基于剪切变换和深层神经网络的无参考图像质量评价的文章源码及论文No-reference image quality assessment with shearlet transform and deep neural networks-No-reference image quality assessment with shearlet transform and deep neural networks
shearletnew
- 改进的剪切波变换，用于图像处理，分解层数多，运行速度快(Improved shear wave transform for image processing, decomposition of many layers, fast running speed)
shearlet_toolbox
- 剪切波公具，用于图像处理，包含剪切波分解和重构，融合效果好(Shear wave is used in image processing, including shear wave decomposition and reconstruction, and the fusion effect is good)
shearlet_denoising
- 利用剪切波变换的方向性和多尺度特性，在剪切波变换域中进行阈值降噪处理(Using the directional and multi-scale characteristics of shearlet, the threshold denoising is performed well in the shearlet transform domain)