13518219792
这个可比你想象的复杂多了，s是个复变量，1/(s+1)极点在-1，要想用C语言写，必须理解清楚下面几个问题：
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1、输入必须是个有限序列，比如(x+yi)，x和y分别是两个长度为N的数组
2、要过滤的频率，必须是个整型值，或者是个整型区间
3、输出结果同样是两个长度为N的数组(p+qi)
4、整个程序需要使用最基本的复数运算，这一点C语言本身不提供，必须手工写复函数运算库
5、实现的时候具体算法还需要编，这里才是你问题的核心。
我可以送你一段FFT的程序，自己琢磨吧，和MATLAB的概念差别很大：
#include assert.h
#include math.h
#include stdio.h
#include stdlib.h
#include string.h
#include windows.h
#include "complex.h"
extern "C" {
// Discrete Fourier Transform (Basic Version, Without Any Enhancement)
// return - Without Special Meaning, constantly, zero
int DFT (long count, CComplex * input, CComplex * output)
{
assert(count);
assert(input);
assert(output);
CComplex F, X, T, W; int n, i;
long N = abs(count); long Inversing = count 0? 1: -1;
for(n = 0; n N ; n++){ // compute from line 0 to N-1
F = CComplex(0.0f, 0.0f); // clear a line
for(i = 0; i N; i++) {
T = input[i];
W = HarmonicPI2(Inversing * n * i, N);
X = T * W;
F += X; // fininshing a line
}//next i
// save data to outpus
memcpy(output + n, F, sizeof(F));
}//next n
return 0;
}//end DFT
int fft (long count, CComplex * input, CComplex * output)
{
assert(count);
assert(input);
assert(output);
int N = abs(count); long Inversing = count 0? -1: 1;
if (N % 2 || N 5) return DFT(count, input, output);
long N2 = N / 2;
CComplex * iEven = new CComplex[N2]; memset(iEven, 0, sizeof(CComplex) * N2);
CComplex * oEven = new CComplex[N2]; memset(oEven, 0, sizeof(CComplex) * N2);
CComplex * iOdd = new CComplex[N2]; memset(iOdd , 0, sizeof(CComplex) * N2);
CComplex * oOdd = new CComplex[N2]; memset(oOdd , 0, sizeof(CComplex) * N2);
int i = 0; CComplex W;
for(i = 0; i N2; i++) {
iEven[i] = input[i * 2];
iOdd [i] = input[i * 2 + 1];
}//next i
fft(N2 * Inversing, iEven, oEven);
fft(N2 * Inversing, iOdd, oOdd );
for(i = 0; i N2; i++) {
W = HarmonicPI2(Inversing * (- i), N);
output[i] = oEven[i] + W * oOdd[i];
output[i + N2] = oEven[i] - W * oOdd[i];
}//next i
return 0;
}//end FFT
void __stdcall FFT(
long N, // Serial Length, N 0 for DFT, N 0 for iDFT - inversed Discrete Fourier Transform
double * inputReal, double * inputImaginary, // inputs
double * AmplitudeFrequences, double * PhaseFrequences) // outputs
{
if (N == 0) return;
if (!inputReal !inputImaginary) return;
short n = abs(N);
CComplex * input = new CComplex[n]; memset(input, 0, sizeof(CComplex) * n);
CComplex * output= new CComplex[n]; memset(output,0, sizeof(CComplex) * n);
double rl = 0.0f, im = 0.0f; int i = 0;
for (i = 0; i n; i++) {
rl = 0.0f; im = 0.0f;
if (inputReal) rl = inputReal[i];
if (inputImaginary) im = inputImaginary[i];
input[i] = CComplex(rl, im);
}//next i
int f = fft(N, input, output);
double factor = n;
//factor = sqrt(factor);
if (N 0)
factor = 1.0f;
else
factor = 1.0f / factor;
//end if
for (i = 0; i n; i++) {
if (AmplitudeFrequences) AmplitudeFrequences[i] = output[i].getReal() * factor;
if (PhaseFrequences) PhaseFrequences[i] = output[i].getImaginary() * factor;
}//next i
delete [] output;
delete [] input;
return ;
}//end FFT
int __cdecl main(int argc, char * argv[])
{
fprintf(stderr, "%s usage:\n", argv[0]);
fprintf(stderr, "Public Declare Sub FFT Lib \"wfft.exe\" \
(ByVal N As Long, ByRef inputReal As Double, ByRef inputImaginary As Double, \
ByRef freqAmplitude As Double, ByRef freqPhase As Double)");
return 0;
}//end main
};//end extern "C"
float middle_filter(float middle_value [] , intcount)
{
float sample_value, data;
int i, j;
for (i=1; i for(j=count-1; j=i,--j){
if(middle_value[j-1]=middle_value[j]{
data=middle_value[j-1];
middle_value[j-1]=middle_value[j]
middle_value[j]=data;
}
}
sample_value=middle_value(count-1)/2];
return(sample_value);
}
1. 是规定做中值滤波的点不含边缘的点(取决于中值滤波窗口大小)。 2,对图像边缘部分的信息进行镜像处理。
