我使用Lanczos重新采样编写了一个图像缩放器。我直接从维基百科上的指示实施。结果在视觉上看起来很好,但由于某些原因,它与Matlab调整Lanczos的结果非常匹配(像素错误)。
有人看到有任何错误吗?这根本不是我的专业领域......
这是我的过滤器(我默认使用Lanczos3):
double lanczos_size_ = 3.0;
inline double sinc(double x) {
double pi = 3.1415926;
x = (x * pi);
if (x < 0.01 && x > -0.01)
return 1.0 + x*x*(-1.0/6.0 + x*x*1.0/120.0);
return sin(x)/x;
}
inline double LanczosFilter(double x) {
if (std::abs(x) < lanczos_size_) {
double pi = 3.1415926;
return sinc(x)*sinc(x/lanczos_size_);
} else {
return 0.0;
}
}
我的代码调整图像大小:
Image Resize(Image& image, int new_rows, int new_cols) {
int old_cols = image.size().cols;
int old_rows = image.size().rows;
double col_ratio =
static_cast<double>(old_cols)/static_cast<double>(new_cols);
double row_ratio =
static_cast<double>(old_rows)/static_cast<double>(new_rows);
// Apply filter first in width, then in height.
Image horiz_image(new_cols, old_rows);
for (int r = 0; r < old_rows; r++) {
for (int c = 0; c < new_cols; c++) {
// x is the new col in terms of the old col coordinates.
double x = static_cast<double>(c)*col_ratio;
// The old col corresponding to the closest new col.
int floor_x = static_cast<int>(x);
horiz_image[r][c] = 0.0;
double weight = 0.0;
// Add up terms across the filter.
for (int i = floor_x - lanczos_size_ + 1; i < floor_x + lanczos_size_; i++) {
if (i >= 0 && i < old_cols) {
double lanc_term = LanczosFilter(x - i);
horiz_image[r][c] += image[r][i]*lanc_term;
weight += lanc_term;
}
}
// Normalize the filter.
horiz_image[r][c] /= weight;
// Strap the pixel values to valid values.
horiz_image[r][c] = (horiz_image[r][c] > 1.0) ? 1.0 : horiz_image[r][c];
horiz_image[r][c] = (horiz_image[r][c] < 0.0) ? 0.0 : horiz_image[r][c];
}
}
// Now apply a vertical filter to the horiz image.
Image new_image(new_cols, new_rows);
for (int r = 0; r < new_rows; r++) {
double x = static_cast<double>(r)*row_ratio;
int floor_x = static_cast<int>(x);
for (int c = 0; c < new_cols; c++) {
new_image[r][c] = 0.0;
double weight = 0.0;
for (int i = floor_x - lanczos_size_ + 1; i < floor_x + lanczos_size_; i++) {
if (i >= 0 && i < old_rows) {
double lanc_term = LanczosFilter(x - i);
new_image[r][c] += horiz_image[i][c]*lanc_term;
weight += lanc_term;
}
}
new_image[r][c] /= weight;
new_image[r][c] = (new_image[r][c] > 1.0) ? 1.0 : new_image[r][c];
new_image[r][c] = (new_image[r][c] < 0.0) ? 0.0 : new_image[r][c];
}
}
return new_image;
}
答案 0 :(得分:2)
这是Lanczosh的单循环。没有错误。 最高程序中提到的用途。
void ResizeDD(
double* const pixelsSrc,
const int old_cols,
const int old_rows,
double* const pixelsTarget,
int const new_rows, int const new_cols)
{
double col_ratio =
static_cast<double>(old_cols) / static_cast<double>(new_cols);
double row_ratio =
static_cast<double>(old_rows) / static_cast<double>(new_rows);
// Now apply a filter to the image.
for (int r = 0; r < new_rows; ++r)
{
const double row_within = static_cast<double>(r)* row_ratio;
int floor_row = static_cast<int>(row_within);
for (int c = 0; c < new_cols; ++c)
{
// x is the new col in terms of the old col coordinates.
double col_within = static_cast<double>(c)* col_ratio;
// The old col corresponding to the closest new col.
int floor_col = static_cast<int>(col_within);
double& v_toSet = pixelsTarget[r * new_cols + c];
v_toSet = 0.0;
double weight = 0.0;
for (int i = floor_row - lanczos_size_ + 1; i <= floor_row + lanczos_size_; ++i)
{
for (int j = floor_col - lanczos_size_ + 1; j <= floor_col + lanczos_size_; ++j)
{
if (i >= 0 && i < old_rows && j >= 0 && j < old_cols)
{
const double lanc_term = LanczosFilter(row_within - i + col_within - j);
v_toSet += pixelsSrc[i * old_rows + j] * lanc_term;
weight += lanc_term;
}
}
}
v_toSet /= weight;
v_toSet = (v_toSet > 1.0) ? 1.0 : v_toSet;
v_toSet = (v_toSet < 0.0) ? 0.0 : v_toSet;
}
}
}
答案 1 :(得分:1)
该行
for (int i = floor_x - lanczos_size_ + 1; i < floor_x + lanczos_size_; i++)
应该是
for (int i = floor_x - lanczos_size_ + 1; i <= floor_x + lanczos_size_; i++)
不知道,但也许其他错误也会延续。
答案 2 :(得分:0)
我认为你的sinc功能有误。在分数栏下方,您必须对pi和x进行平方。另外,你必须将函数乘以lanczos大小
L(x) = **a***sin(pi*x)*sin(pi*x/a) * (pi**²**x**²**)^-1
编辑:我的错误,没事。