dft

I aim to get the DFT of an image in OpenCV. Using dft function, I'm able to calculate it, and then paint it by calculating its magnitude (then, apply the log and finally normalize it in order to paint values between 0 and 1). My result is, for the following image, the result I show you (with swap in order to have lower frequencies in the center of the image): However, if I compare it to the result I obtain using other tools like Halcon , It seems incorrect to my since It seems to have really "high" values (the OpenCV DFT magnitude I mean): I thought it might be for these reasons: The

如Coding 时需要考虑什么样的代码风格会使gating 的效率更高；综合时需要特别设置要插入的gating 类型，每个gating 的fanout 范围，是否可以跨层次，是否需要做physical aware 的gating；DFT 时需要确定clock gating 的TE pin 如何连接以保证在DFT 模式clock 可控；P&R 时需要考虑gating 的位置是否合理，是否由于物理上相互拉扯做gating 复制，是否需要人工约束某些关键gating 的位置，CTS 是否需要对gating 做特殊处理；形式验证时需要对gating 做特殊建模；STA时需要明白工具在什么情况下会映射出clock gating 的check, 哪些是真实的clock gating 哪些又是假的clock gating。 Icer 发明了Gating 这一技能，在不需要时钟翻转的时候就把时钟关掉，据统计这一技术可以减少20%的动态功耗。Clock gating 并不是一日长成今日这般模样，经过各种摸索尝试之后，时至今日使用最广泛的clock gating cell 就是glitch free 的Integrated clock-gating cell (ICG). 目前主流工艺Foundry 提供的库中都有ICG cell. 来源： https://www.cnblogs.com/lelin

驴：在16nm之前数字实现工程师做STA很少会做跟SPICE的correlation，进入16nm之后，这一步似乎变得必不可少了，随着工艺的进步，STA方法学上有哪些大的改进？ [Jes]:Spice correlation主要校样的是延时计算， 这一块在深亚微米上有两个方面复杂度的增加： Voltage waveform的非线性特性； OCV的计算开始进入统计计算的时代。 所以这使得人工手动校样工具的延时值变得困难， 由dump出来的spice deck, 自动跑仿真校样会比较有效。另一方面， 芯片主频的提高也使得STA的margin越来越小， 关键路径上的spice仿真增加精准度， 释放一部分被简化模型吃掉的margin。 驴：Tempus在深亚微米的优势是什么？ [Jes]:16nm之后，C家以Innovus牵头的数字实现full flow都优势显著，就Tempus可以概括为以下三点： Spice simulation：Tempus 从真实的design中dump的spice deck清晰明了， 简单易懂， 可以直接拿来做时序仿真； 工艺的优化带来集成度的进一步提升，对flatten signoff的runtime有巨大的挑战； 时钟设计的复杂度也对hier signoff 的精度有更高的要求。 Tempus坚持以flatten signoff为基石，

I want to implement follwoing OpenCV example in a Android app: http://docs.opencv.org/doc/tutorials/core/discrete_fourier_transform/discrete_fourier_transform.html My code is the foll0wing: //First convert Bitmap to Mat Mat ImageMat = new Mat ( image.getHeight(), image.getWidth(), CvType.CV_64FC1, new Scalar(4)); Bitmap myBitmap32 = image.copy(Bitmap.Config.ARGB_8888, true); Utils.bitmapToMat(myBitmap32, ImageMat); Imgproc.cvtColor(ImageMat, ImageMat, Imgproc.COLOR_RGB2GRAY); Mat padded = new Mat(CvType.CV_64FC1); //expand input image to optimal size int m = Core.getOptimalDFTSize(ImageMat

My main goal is to show that the convolution theorem works (just a reminder: the convolution theorem means that idft(dft(im) .* dft(mask)) = conv(im, mask) ). I'm trying to program that. Here is my code: function displayTransform( im ) % This routine displays the Fourier spectrum of an image. % % Input: im - a grayscale image (values in [0,255]) % % Method: Computes the Fourier transform of im and displays its spectrum, % (if F(u,v) = a+ib, displays sqrt(a^2+b^2)). % Uses display techniques for visualization: log, and stretch values to full range, % cyclic shift DC to center (use fftshift). %

题解 代码 1 #include <cstdio> 2 #include <istream> 3 #define ll long long 4 using namespace std; 5 const ll N=200010,mo=786433; 6 int T,n,m,k,x,y,sum,num[N],f[N],len,L,rev[N],v[10][N]; 7 ll a[10][N],inv[mo],fac[N],ny[N],ans[N],p[N],P[N]; 8 char s[N]; 9 ll ksm(ll a,ll b) { ll r=1; for (;b;b>>=1,a=a*a%mo) if (b&1) r=r*a%mo; return r; } 10 void ntt(ll *a,int len,int f) 11 { 12 for (int i=0;i<len;i++) if (rev[i]>i) swap(a[i],a[rev[i]]); 13 for (int i=2;i<=len;i<<=1) 14 { 15 ll r,m=i>>1; 16 if (f==1) r=p[i]; else r=P[i]; 17 for (int j=0;j<len;j+=i) 18 { 19 ll R=1; 20 for (int k=0;k<m;k++,R=R*r%mo) 21 {

I've been playing around a little with the Exocortex implementation of the FFT, but I'm having some problems. Whenever I modify the amplitudes of the frequency bins before calling the iFFT the resulting signal contains some clicks and pops, especially when low frequencies are present in the signal (like drums or basses). However, this does not happen if I attenuate all the bins by the same factor. Let me put an example of the output buffer of a 4-sample FFT: // Bin 0 (DC) FFTOut[0] = 0.0000610351563 FFTOut[1] = 0.0 // Bin 1 FFTOut[2] = 0.000331878662 FFTOut[3] = 0.000629425049 // Bin 2 FFTOut

I am new in OpenCV and image processing algorithms. I need to do inverse discrete fourier transformation in OpenCV in C++, but I don't know how. I searched over internet and I didn't find answer. I am doing fourier transformation in my program with this code from this page: http://opencv.itseez.com/doc/tutorials/core/discrete_fourier_transform/discrete_fourier_transform.html . I have tried to do inverse to that code, but I don't know where I am doing wrong. My code is here (I think that whole code is wrong): void doFourierInverse(const Mat &src, Mat &dst) { normalize(src, dst, 0, -1, CV_MINMAX