tuxiangbianh

此程序实现图象傅立叶变换、离散傅立叶变换和小波变换、可以自己设定小波变换的参数(this procedure image Fourier transform, discrete Fourier transform and wavelet transform, can set their own parameters of wavelet transform)

复杂杂波背景下，海面红外图像中舰船目标检测的关键是如何降低虚警、检测出真正的 目标。为了实现这个目标，首先利用Haar小波函数进行小波变换，对图像进行预处理；然后进行恒 虚警检测（CFAR）；第三步采用行程法对检测的目标进行标记；最后采用管道滤波法剔出虚假目标， 检测出真正的目标。经仿真实验证明，此方法能有效地降低虚警，在复杂杂波背景中检测出目标。 (The key of ship detection in sea infrared image with complex clutter background is how to reduce false alarms and detect true targets.In order to achieve this aim,a novel algorithm is proposed. First,Haar wavelet transform technology is used as preprocessing,then constant false alarm rate(CFAR) detetion,then run-length label to mark objects detected by the above step,finally pipeline filter to reduce false alarms and detect true targets.Experiment results indicate that this method can effectively reduce false alarms and detect true targets in complicated sea background. )

说明：  整数Haar小波变换和逆变换 可用于差值扩展等(Integer Haar wavelet transform and inverter)

加速度振动信号积分为速度振动信号输出，如需要可再一次积分，变换为位移信号(Vibration signal of the acceleration integral transform)

基于TS变换（一种整数变换）所实现的脆弱数字水印方法，讨论了整数水印在数字水印中的优点(transform (a Integer Transform), the fragile digital watermarking methods, Integer discussed in the digital watermark of the benefits of watermarking)

Harr小波具有紧支撑、零调和性和优良的正交性等优点，因此选择它作为小波变换的基函数。对采样数据进行4层小波包变换得到16维的特征样本。(Harr wavelet has compact support, zero reconcile and excellent orthogonality, etc., so choose it as the base function of wavelet transform. 4 layers of sampled data wavelet packet transform feature samples 16 dimensions.)

包含压缩传感的随机矩阵程序，如小波变换和高斯随机矩阵和omp重构算法(Random matrix containing the compressed sensing programs, such as wavelet transform and Gaussian random matrices and omp reconstruction algorithm)

对信号进行小波去噪再重构，针对不同分解层次的高频系数，采用分层阈值方式来量化高频系数。(Signal is carried out the wavelet denoising again reconstructed to quantify high-frequency coefficients, high-frequency coefficients for different levels of decomposition, hierarchical threshold.)

Block-based random image sampling is coupled with a projectiondriven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image. Both contourlets as well as complex-valued dual-tree wavelets are considered for their highly directional representation, while bivariate shrinkage is adapted to their multiscale decomposition structure to provide the requisite sparsity constraint. Smoothing is achieved via a Wiener filter incorporated into iterative projected Landweber compressed-sensing recovery, yielding fast reconstruction. The proposed approach yields images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation. Additionally, reconstruction quality is substantially superior to that from several prominent pursuits-based algorithms that do not include any smoothing

说明：  Fault Location in Extra Long HVdc Transmission Lines using Continuous Wavelet Transform