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Spatial denoising; too much blur

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Haohua-Lyu 3 years ago
parent 9e682d4ab1
commit 959d572aae
2 changed files with 102 additions and 12 deletions
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67
include/hdrplus/merge.h
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@ -96,6 +96,71 @@ class merge
return img;
}
void circshift(cv::Mat &out, const cv::Point &delta)
{
cv::Size sz = out.size();
// error checking
assert(sz.height > 0 && sz.width > 0);
// no need to shift
if ((sz.height == 1 && sz.width == 1) || (delta.x == 0 && delta.y == 0))
return;
// delta transform
int x = delta.x;
int y = delta.y;
if (x > 0) x = x % sz.width;
if (y > 0) y = y % sz.height;
if (x < 0) x = x % sz.width + sz.width;
if (y < 0) y = y % sz.height + sz.height;
// in case of multiple dimensions
std::vector<cv::Mat> planes;
split(out, planes);
for (size_t i = 0; i < planes.size(); i++)
{
// vertical
cv::Mat tmp0, tmp1, tmp2, tmp3;
cv::Mat q0(planes[i], cv::Rect(0, 0, sz.width, sz.height - y));
cv::Mat q1(planes[i], cv::Rect(0, sz.height - y, sz.width, y));
q0.copyTo(tmp0);
q1.copyTo(tmp1);
tmp0.copyTo(planes[i](cv::Rect(0, y, sz.width, sz.height - y)));
tmp1.copyTo(planes[i](cv::Rect(0, 0, sz.width, y)));
// horizontal
cv::Mat q2(planes[i], cv::Rect(0, 0, sz.width - x, sz.height));
cv::Mat q3(planes[i], cv::Rect(sz.width - x, 0, x, sz.height));
q2.copyTo(tmp2);
q3.copyTo(tmp3);
tmp2.copyTo(planes[i](cv::Rect(x, 0, sz.width - x, sz.height)));
tmp3.copyTo(planes[i](cv::Rect(0, 0, x, sz.height)));
}
cv::merge(planes, out);
}
void fftshift(cv::Mat &out)
{
cv::Size sz = out.size();
cv::Point pt(0, 0);
pt.x = (int) floor(sz.width / 2.0);
pt.y = (int) floor(sz.height / 2.0);
circshift(out, pt);
}
void ifftshift(cv::Mat &out)
{
cv::Size sz = out.size();
cv::Point pt(0, 0);
pt.x = (int) ceil(sz.width / 2.0);
pt.y = (int) ceil(sz.height / 2.0);
circshift(out, pt);
}
std::vector<cv::Mat> getReferenceTiles(cv::Mat reference_image);
cv::Mat mergeTiles(std::vector<cv::Mat> tiles, int rows, int cols);
@ -109,7 +174,7 @@ class merge
//temporal denoise
std::vector<cv::Mat> temporal_denoise(std::vector<cv::Mat> reference_tiles, std::vector<cv::Mat> reference_tiles_DFT, std::vector<float> noise_varaince);
std::vector<cv::Mat> spatial_denoise(std::vector<cv::Mat> reference_tiles, std::vector<cv::Mat> reference_tiles_DFT, std::vector<float> noise_varaince);
std::vector<cv::Mat> spatial_denoise(std::vector<cv::Mat> tiles, int num_alts, std::vector<float> noise_variance, float spatial_factor);
};

47
src/merge.cpp
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@ -97,6 +97,7 @@ namespace hdrplus
cv::Range horizontal = cv::Range(padding[2], reference_image.cols - padding[3]);
cv::Range vertical = cv::Range(padding[0], reference_image.rows - padding[1]);
burst_images.merged_bayer_image = merged(vertical, horizontal);
cv::imwrite("merged.jpg", burst_images.merged_bayer_image);
}
std::vector<cv::Mat> merge::getReferenceTiles(cv::Mat reference_image) {
@ -192,14 +193,14 @@ namespace hdrplus
////adding after here
//std::vector<cv::Mat> spatial_denoised_tiles = spatial_denoise( reference_tiles, reference_tiles_DFT, noise_varaince)
std::vector<cv::Mat> spatial_denoised_tiles = spatial_denoise(reference_tiles_DFT, alternate_channel_i_list.size(), noise_variance, 0.1);
//apply the cosineWindow2D over the merged_channel_tiles_spatial and reconstruct the image
//reference_tiles = spatial_denoised_tiles; //now reference tiles are temporally and spatially denoised
reference_tiles = spatial_denoised_tiles; //now reference tiles are temporally and spatially denoised
////
reference_tiles_DFT = spatial_denoised_tiles;
// Apply IFFT on reference tiles (frequency to spatial)
std::vector<cv::Mat> denoised_tiles;
for (auto dft_tile : reference_tiles_DFT) {
for (auto dft_tile : reference_tiles) {
cv::Mat denoised_tile;
cv::dft(dft_tile, denoised_tile, cv::DFT_INVERSE | cv::DFT_REAL_OUTPUT);
denoised_tile.convertTo(denoised_tile, CV_16U);
@ -351,18 +352,42 @@ namespace hdrplus
}
// std::vector<cv::Mat> spatial_denoise(std::vector<cv::Mat> reference_tiles, std::vector<cv::Mat> reference_tiles_DFT, std::vector<float> noise_varaince) {
std::vector<cv::Mat> merge::spatial_denoise(std::vector<cv::Mat> tiles, int num_alts, std::vector<float> noise_variance, float spatial_factor) {
// double spatial_factor = 1; //to be added
// double spatial_noise_scaling = (pow(TILE_SIZE,2) * (1.0/16*2))*spatial_factor;
double spatial_noise_scaling = (TILE_SIZE * TILE_SIZE * (1.0 / 16)) * spatial_factor;
// //calculate the spatial denoising
// spatial_tile_dist = reference_tiles.real**2 + reference_tiles.imag**2;
// std::vector<cv::Mat> WienerCoeff = denoised_tiles*spatial_noise_scaling*noise_variance;
// Calculate |w| using ifftshift
cv::Mat row_distances = cv::Mat::zeros(1, TILE_SIZE, CV_32F);
for(int i = 0; i < TILE_SIZE; ++i) {
row_distances.at<float>(i) = i - offset;
}
row_distances = cv::repeat(row_distances.t(), 1, TILE_SIZE);
cv::Mat col_distances = row_distances.t();
cv::Mat distances;
cv::sqrt(row_distances.mul(row_distances) + col_distances.mul(col_distances), distances);
ifftshift(distances);
std::vector<cv::Mat> denoised;
// Loop through all tiles
for (int i = 0; i < tiles.size(); ++i) {
cv::Mat tile = tiles[i];
float coeff = noise_variance[i] / num_alts * spatial_noise_scaling;
// merged_channel_tiles_spatial = reference_tiles*spatial_tile_dist/(spatial_tile_dist+WienerCoeff)
// Calculate absolute difference
cv::Mat complexMats[2];
cv::split(tile, complexMats); // planes[0] = Re(DFT(I)), planes[1] = Im(DFT(I))
cv::magnitude(complexMats[0], complexMats[1], complexMats[0]); // planes[0] = magnitude
cv::Mat absolute_diff = complexMats[0].mul(complexMats[0]);
// Division
cv::Mat scale;
cv::divide(absolute_diff, absolute_diff + distances * coeff, scale);
cv::merge(std::vector<cv::Mat>{scale, scale}, scale);
denoised.push_back(tile.mul(scale));
}
// }
return denoised;
}
std::pair<double, double> merge::getNoiseParams( int ISO, \

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