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我试图确保FFTW做我认为应该做的事情，但遇到问题。我正在使用OpenCV的cv::Mat。我制定了一个测试程序，在Mat f的基础上，计算ifft(fft(f))并将结果与f进行比较。我希望两者之间的差异是可以忽略的，但有在数据中的怪图案..fft的fft未返回预期数据
在这种情况下，f被初始化为具有正的值的浮子的8×8阵列小于1
这里是我的测试程序代码：
Mat f = .. //populate f
if (f.type() != CV_32FC1)
DLOG << "Bad f type";
const int y = f.rows;
const int x = f.cols;
double* input = fftw_alloc_real(y * 2*(x/2 + 1));
// forward fft
fftw_plan plan = fftw_plan_dft_r2c_2d(x, y, input, (fftw_complex*)input, FFTW_MEASURE);
// inverse fft
fftw_plan iplan = fftw_plan_dft_c2r_2d(x, y, (fftw_complex*)input, input, FFTW_MEASURE);
// populate fftw data from f
for (int yi = 0; yi < y; ++yi)
{
const float* yptr = f.ptr<float>(yi);
for (int xi = 0; xi < x; ++xi)
input[yi*x + xi] = (double)yptr[xi];
}
fftw_execute(plan);
fftw_execute(iplan);
// put data into another cv::Mat for comparison
Mat check(y, x, f.type());
for (int yi = 0; yi < y; ++yi)
{
float* yptr = check.ptr<float>(yi);
for (int xi = 0; xi < x ; ++xi)
yptr[xi] = (float)input[yi*x + xi];
}
DLOG << Util::summary(f, "f");
DLOG << f;
DLOG << Util::summary(check, "check");
DLOG << check;
Mat diff = f*x*y - check;
DLOG << Util::summary(diff, "diff");
DLOG << diff;
凡DLOG是我记录仪和Util::summary(cv::Mat m)只是打印传递的字符串和尺寸，渠道，最小值和最大值的垫子。
下面是数据的模样（输出）：
f: rows:8 cols:8 chans:1 min:0.00257996 max:0.4
[0.050668437, 0.04509116, 0.033668514, 0.10986148, 0.12855141, 0.048241843, 0.12613985,.09731093;
0.028602425, 0.0092236707, 0.037089188, 0.118964, 0.075040311, 0.40000001, 0.11959606, 0.071930833;
0.0025799556, 0.051522054, 0.22233701, 0.052993439, 0.032000393, 0.12673819, 0.015244827, 0.044803992;
0.13946071, 0.019708242, 0.0112687, 0.047459811, 0.019342113, 0.030085485, 0.018739942, 0.0098618753;
0.041809395, 0.029681522, 0.026837418, 0.16038358, 0.29034778, 0.17247421, 0.1789207, 0.042179305;
0.025630442, 0.017192598, 0.060540862, 0.1854037, 0.21287154, 0.04813192, 0.042614728, 0.034764063;
0.0030835248, 0.018511582, 0.0071733585, 0.017076733, 0.064545207, 0.0026390438, 0.088922881, 0.045725599;
0.12798512, 0.23215951, 0.027465452, 0.03174505, 0.04352935, 0.025079668, 0.044403922, 0.035459157]
check: rows:8 cols:8 chans:1 min:-3.26489 max:25.6
[3.24278, 2.8858342, 2.1547849, 7.0311346, 8.2272902, 3.0874779, 8.0729504, 6.2278996;
0.30818239, 0, 2.373708, 7.6136961, 4.8025799, 25.6, 7.6541481, 4.6035733;
0.16511716, 3.2974114, -3.2648909, 0, 2.0480251, 8.1112442, 0.97566891, 2.8674555;
8.9254856, 1.2613275, 0.72119683, 3.0374279, -0.32588482, 0, 1.1993563, 0.63116002;
2.6758013, 1.8996174, 1.7175947, 10.264549, 18.582258, 11.038349, 0.042666838, 0;
1.6403483, 1.1003263, 3.8746152, 11.865837, 13.623778, 3.0804429, 2.7273426, 2.2249;
0.44932228, 0, 0.45909494, 1.0929109, 4.1308932, 0.16889881, 5.6910644, 2.9264383;
8.1910477, 14.858209, -0.071794562, 0, 2.7858784, 1.6050987, 2.841851, 2.2693861]
diff: rows:8 cols:8 chans:1 min:-0.251977 max:17.4945
[0, 0, 0, 0, 0, 0, 0, 0;
1.5223728, 0.59031492, 0, 0, 0, 0, 0, 0;
0, 0, 17.494459, 3.3915801, 0, 0, 0, 0;
0, 0, 0, 0, 1.5637801, 1.9254711, 0, 0;
0, 0, 0, 0, 0, 0, 11.408258, 2.6994755;
0, 0, 0, 0, 0, 0, 0, 0;
-0.2519767, 1.1847413, 0, 0, 0, 0, 0, 0;
0, 0, 1.8295834, 2.0316832, 0, 0, 0, 0]
对我来说，困难的部分是在diff矩阵中的非零项。我已经考虑了FFTW对值和填充需要做的实际数据的原地fft的缩放比例;我错过了什么？
我发现令人惊讶的是，当数据太多时，数据可能会被17的值（这是最大值的66％）关闭。而且，数据不规则性似乎形成了对角线模式。