https://blog.csdn.net/cyh706510441/article/details/45194223
本文介紹了TV模型的基本原理，并給出了C++代碼實現(xiàn)。
一、TV模型原理
二、C++實現(xiàn)
關(guān)于Matlab的程序?qū)崿F(xiàn)，有一個經(jīng)典的主頁： http://visl.technion.ac.il/~gilboa/PDE-filt/tv_denoising.html
有博主改成了C++代碼：見經(jīng)典的變分法圖像去噪的C++實現(xiàn)
另有博主改成了更簡潔的版本：見【圖像處理】全分發(fā)TV圖像去噪
但精簡版的有個問題：image[i][j] += dt*(tmp_num/tmp_den+ lam*(image0[i][j] - image[i][j]));
直接在image中迭代，這有問題，最后得到的去噪圖像跟MATLAB得到的去噪圖像有細微差別，對兩幅圖像做差值可發(fā)現(xiàn)差別。
本文代碼基本參照上面的版本，把代碼修改為（之前公式有誤，已修改 2015年4月23日）：
void CImageObj::Total_Variation(int iter, double dt, double epsilon, double lambda)
{
int i, j;
int nx = m_width, ny = m_height;
double ep2 = epsilon * epsilon;
double** I_t = NewDoubleMatrix(nx, ny);
double** I_tmp = NewDoubleMatrix(nx, ny);
for (i = 0; i < ny; i++)
for (j = 0; j < nx; j++)
I_t[i][j] = I_tmp[i][j] = (double)m_imgData[i][j];
for (int t = 0; t < iter; t++)
{
for (i = 0; i < ny; i++)
{
for (j = 0; j < nx; j++)
{
int iUp = i - 1, iDown = i + 1;
int jLeft = j - 1, jRight = j + 1; // 邊界處理
if (0 == i) iUp = i; if (ny - 1 == i) iDown = i;
if (0 == j) jLeft = j; if (nx - 1 == j) jRight = j;
double tmp_x = (I_t[i][jRight] - I_t[i][jLeft]) / 2.0;
double tmp_y = (I_t[iDown][j] - I_t[iUp][j]) / 2.0;
double tmp_xx = I_t[i][jRight] + I_t[i][jLeft] - 2 * I_t[i][j];
double tmp_yy = I_t[iDown][j] + I_t[iUp][j] - 2 * I_t[i][j];
double tmp_xy = (I_t[iDown][jRight] + I_t[iUp][jLeft] - I_t[iUp][jRight] - I_t[iDown][jLeft]) / 4.0;
double tmp_num = tmp_yy * (tmp_x * tmp_x + ep2) + tmp_xx * (tmp_y * tmp_y + ep2) - 2 * tmp_x * tmp_y * tmp_xy;
double tmp_den = pow(tmp_x * tmp_x + tmp_y * tmp_y + ep2, 1.5);
I_tmp[i][j] += dt*(tmp_num / tmp_den + lambda*(m_imgData[i][j] - I_t[i][j]));
}
} // 一次迭代
for (i = 0; i < ny; i++)
for (j = 0; j < nx; j++)
{
I_t[i][j] = I_tmp[i][j];
}
} // 迭代結(jié)束
// 給圖像賦值
for (i = 0; i < ny; i++)
for (j = 0; j < nx; j++)
{
double tmp = I_t[i][j];
tmp = max(0, min(tmp, 255));
m_imgData[i][j] = (unsigned char)tmp;
}
DeleteDoubleMatrix(I_t, nx, ny);
DeleteDoubleMatrix(I_tmp, nx, ny);
}
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作者：cyh706510441
來源：CSDN
原文：https://blog.csdn.net/cyh706510441/article/details/45194223
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