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Added code in progress for temporal and spatial denoising

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I added some of my code for temporal and spatial denoising, but I commented it out since it still is incomplete since I need to find a way to allow for multiple image channels. I created a helper function which we could use to return the channels. I also edited the merge.h file to include the signature of the helper function.
main
cvachha 3 years ago committed by Haohua-Lyu
parent 48a7489859
commit 050d4ca4f3
1 changed files with 238 additions and 134 deletions
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src/merge.cpp
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@ -397,169 +397,273 @@ std::pair<double, double> merge::getNoiseParams( int ISO, \
} else {
channels[1].push_back(reference_image.at<ushort>(y, x));
}
} else {
if (x % 2 == 0) {
channels[2].push_back(reference_image.at<ushort>(y, x));
} else {
channels[3].push_back(reference_image.at<ushort>(y, x));
else {
if (x % 2 == 0) {
channels[2].push_back(reference_image.at<ushort>(y, x));
}
else {
channels[3].push_back(reference_image.at<ushort>(y, x));
}
}
}
}
}
// For each channel, perform denoising and merge
for (int i = 0; i < 4; ++i) {
// Get channel mat
cv::Mat channel_i(reference_image.rows / 2, reference_image.cols / 2, CV_16U, channels[i].data());
// cv::imwrite("ref" + std::to_string(i) + ".jpg", channel_i);
// For each channel, perform denoising and merge
for (int i = 0; i < 4; ++i) {
// Get channel mat
cv::Mat channel_i(reference_image.rows / 2, reference_image.cols / 2, CV_16U, channels[i].data());
// cv::imwrite("ref" + std::to_string(i) + ".jpg", channel_i);
// Apply merging on the channel
cv::Mat merged_channel = processChannel(burst_images, alignments, channel_i, lambda_shot, lambda_read);
// cv::imwrite("merged" + std::to_string(i) + ".jpg", merged_channel);
// Apply merging on the channel
//we should be getting the individual channel in the same place where we call the processChannel function with the reference channel in its arguments
//possibly we could add another argument in the processChannel function which is the channel_i for the alternate image. maybe using a loop to cover all the other images
// Put channel raw data back to channels
channels[i] = merged_channel.reshape(1, merged_channel.total());
}
cv::Mat merged_channel = processChannel(burst_images, alignments, channel_i, lambda_shot, lambda_read);
// cv::imwrite("merged" + std::to_string(i) + ".jpg", merged_channel);
// Write all channels back to a bayer mat
std::vector<ushort> merged_raw;
// Put channel raw data back to channels
channels[i] = merged_channel.reshape(1, merged_channel.total());
}
for (int y = 0; y < reference_image.rows; ++y) {
for (int x = 0; x < reference_image.cols; ++x) {
if (y % 2 == 0) {
if (x % 2 == 0) {
merged_raw.push_back(channels[0][(y/2)*(reference_image.cols/2) + (x/2)]);
} else {
merged_raw.push_back(channels[1][(y/2)*(reference_image.cols/2) + (x/2)]);
// Write all channels back to a bayer mat
std::vector<ushort> merged_raw;
for (int y = 0; y < reference_image.rows; ++y) {
for (int x = 0; x < reference_image.cols; ++x) {
if (y % 2 == 0) {
if (x % 2 == 0) {
merged_raw.push_back(channels[0][(y / 2) * (reference_image.cols / 2) + (x / 2)]);
}
else {
merged_raw.push_back(channels[1][(y / 2) * (reference_image.cols / 2) + (x / 2)]);
}
}
} else {
if (x % 2 == 0) {
merged_raw.push_back(channels[2][(y/2)*(reference_image.cols/2) + (x/2)]);
} else {
merged_raw.push_back(channels[3][(y/2)*(reference_image.cols/2) + (x/2)]);
else {
if (x % 2 == 0) {
merged_raw.push_back(channels[2][(y / 2) * (reference_image.cols / 2) + (x / 2)]);
}
else {
merged_raw.push_back(channels[3][(y / 2) * (reference_image.cols / 2) + (x / 2)]);
}
}
}
}
// Create merged mat
cv::Mat merged(reference_image.rows, reference_image.cols, CV_16U, merged_raw.data());
// cv::imwrite("merged.jpg", merged);
// Remove padding
std::vector<int> padding = burst_images.padding_info_bayer;
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);
}
// Create merged mat
cv::Mat merged(reference_image.rows, reference_image.cols, CV_16U, merged_raw.data());
// cv::imwrite("merged.jpg", merged);
// Remove padding
std::vector<int> padding = burst_images.padding_info_bayer;
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);
}
std::vector<cv::Mat> merge::getReferenceTiles(cv::Mat reference_image) {
std::vector<cv::Mat> reference_tiles;
for (int y = 0; y < reference_image.rows - offset; y += offset) {
for (int x = 0; x < reference_image.cols - offset; x += offset) {
cv::Mat tile = reference_image(cv::Rect(x, y, TILE_SIZE, TILE_SIZE));
reference_tiles.push_back(tile);
std::vector<cv::Mat> merge::getReferenceTiles(cv::Mat reference_image) {
std::vector<cv::Mat> reference_tiles;
for (int y = 0; y < reference_image.rows - offset; y += offset) {
for (int x = 0; x < reference_image.cols - offset; x += offset) {
cv::Mat tile = reference_image(cv::Rect(x, y, TILE_SIZE, TILE_SIZE));
reference_tiles.push_back(tile);
}
}
return reference_tiles;
}
return reference_tiles;
}
cv::Mat merge::mergeTiles(std::vector<cv::Mat> tiles, int num_rows, int num_cols){
// 1. get all four subsets: original (evenly split), horizontal overlapped,
// vertical overlapped, 2D overlapped
std::vector<std::vector<cv::Mat>> tiles_original;
for (int y = 0; y < num_rows / offset - 1; y += 2) {
std::vector<cv::Mat> row;
for (int x = 0; x < num_cols / offset - 1; x += 2) {
row.push_back(tiles[y * (num_cols / offset - 1) + x]);
cv::Mat merge::mergeTiles(std::vector<cv::Mat> tiles, int num_rows, int num_cols) {
// 1. get all four subsets: original (evenly split), horizontal overlapped,
// vertical overlapped, 2D overlapped
std::vector<std::vector<cv::Mat>> tiles_original;
for (int y = 0; y < num_rows / offset - 1; y += 2) {
std::vector<cv::Mat> row;
for (int x = 0; x < num_cols / offset - 1; x += 2) {
row.push_back(tiles[y * (num_cols / offset - 1) + x]);
}
tiles_original.push_back(row);
}
tiles_original.push_back(row);
}
std::vector<std::vector<cv::Mat>> tiles_horizontal;
for (int y = 0; y < num_rows / offset - 1; y += 2) {
std::vector<cv::Mat> row;
for (int x = 1; x < num_cols / offset - 1; x += 2) {
row.push_back(tiles[y * (num_cols / offset - 1) + x]);
std::vector<std::vector<cv::Mat>> tiles_horizontal;
for (int y = 0; y < num_rows / offset - 1; y += 2) {
std::vector<cv::Mat> row;
for (int x = 1; x < num_cols / offset - 1; x += 2) {
row.push_back(tiles[y * (num_cols / offset - 1) + x]);
}
tiles_horizontal.push_back(row);
}
tiles_horizontal.push_back(row);
}
std::vector<std::vector<cv::Mat>> tiles_vertical;
for (int y = 1; y < num_rows / offset - 1; y += 2) {
std::vector<cv::Mat> row;
for (int x = 0; x < num_cols / offset - 1; x += 2) {
row.push_back(tiles[y * (num_cols / offset - 1) + x]);
std::vector<std::vector<cv::Mat>> tiles_vertical;
for (int y = 1; y < num_rows / offset - 1; y += 2) {
std::vector<cv::Mat> row;
for (int x = 0; x < num_cols / offset - 1; x += 2) {
row.push_back(tiles[y * (num_cols / offset - 1) + x]);
}
tiles_vertical.push_back(row);
}
tiles_vertical.push_back(row);
}
std::vector<std::vector<cv::Mat>> tiles_2d;
for (int y = 1; y < num_rows / offset - 1; y += 2) {
std::vector<cv::Mat> row;
for (int x = 1; x < num_cols / offset - 1; x += 2) {
row.push_back(tiles[y * (num_cols / offset - 1) + x]);
std::vector<std::vector<cv::Mat>> tiles_2d;
for (int y = 1; y < num_rows / offset - 1; y += 2) {
std::vector<cv::Mat> row;
for (int x = 1; x < num_cols / offset - 1; x += 2) {
row.push_back(tiles[y * (num_cols / offset - 1) + x]);
}
tiles_2d.push_back(row);
}
tiles_2d.push_back(row);
}
// 2. Concatenate the four subsets
cv::Mat img_original = cat2Dtiles(tiles_original);
cv::Mat img_horizontal = cat2Dtiles(tiles_horizontal);
cv::Mat img_vertical = cat2Dtiles(tiles_vertical);
cv::Mat img_2d = cat2Dtiles(tiles_2d);
// 3. Add the four subsets together
img_original(cv::Rect(offset, 0, num_cols - TILE_SIZE, num_rows)) += img_horizontal;
img_original(cv::Rect(0, offset, num_cols, num_rows - TILE_SIZE)) += img_vertical;
img_original(cv::Rect(offset, offset, num_cols - TILE_SIZE, num_rows - TILE_SIZE)) += img_2d;
return img_original;
}
cv::Mat merge::processChannel( hdrplus::burst& burst_images, \
std::vector<std::vector<std::vector<std::pair<int, int>>>>& alignments, \
cv::Mat channel_image, \
float lambda_shot, \
float lambda_read) {
// Get tiles of the reference image
std::vector<cv::Mat> reference_tiles = getReferenceTiles(channel_image);
// Get noise variance (sigma**2 = lambda_shot * tileRMS + lambda_read)
std::vector<float> noise_variance = getNoiseVariance(reference_tiles, lambda_shot, lambda_read);
// Apply FFT on reference tiles (spatial to frequency)
std::vector<cv::Mat> reference_tiles_DFT;
for (auto ref_tile : reference_tiles) {
cv::Mat ref_tile_DFT;
ref_tile.convertTo(ref_tile_DFT, CV_32F);
cv::dft(ref_tile_DFT, ref_tile_DFT, cv::DFT_SCALE|cv::DFT_COMPLEX_OUTPUT);
reference_tiles_DFT.push_back(ref_tile_DFT);
// 2. Concatenate the four subsets
cv::Mat img_original = cat2Dtiles(tiles_original);
cv::Mat img_horizontal = cat2Dtiles(tiles_horizontal);
cv::Mat img_vertical = cat2Dtiles(tiles_vertical);
cv::Mat img_2d = cat2Dtiles(tiles_2d);
// 3. Add the four subsets together
img_original(cv::Rect(offset, 0, num_cols - TILE_SIZE, num_rows)) += img_horizontal;
img_original(cv::Rect(0, offset, num_cols, num_rows - TILE_SIZE)) += img_vertical;
img_original(cv::Rect(offset, offset, num_cols - TILE_SIZE, num_rows - TILE_SIZE)) += img_2d;
return img_original;
}
// TODO: 4.2 Temporal Denoising
cv::Mat merge::processChannel(hdrplus::burst& burst_images, \
std::vector<std::vector<std::vector<std::pair<int, int>>>>& alignments, \
cv::Mat channel_image, \
float lambda_shot, \
float lambda_read) {
// Get tiles of the reference image
std::vector<cv::Mat> reference_tiles = getReferenceTiles(channel_image);
// TODO: 4.3 Spatial Denoising
// Get noise variance (sigma**2 = lambda_shot * tileRMS + lambda_read)
std::vector<float> noise_variance = getNoiseVariance(reference_tiles, lambda_shot, lambda_read);
// Apply FFT on reference tiles (spatial to frequency)
std::vector<cv::Mat> reference_tiles_DFT;
for (auto ref_tile : reference_tiles) {
cv::Mat ref_tile_DFT;
ref_tile.convertTo(ref_tile_DFT, CV_32F);
cv::dft(ref_tile_DFT, ref_tile_DFT, cv::DFT_SCALE | cv::DFT_COMPLEX_OUTPUT);
reference_tiles_DFT.push_back(ref_tile_DFT);
}
// Apply IFFT on reference tiles (frequency to spatial)
std::vector<cv::Mat> denoised_tiles;
for (auto dft_tile : reference_tiles_DFT) {
cv::Mat denoised_tile;
cv::dft(dft_tile, denoised_tile, cv::DFT_INVERSE|cv::DFT_REAL_OUTPUT);
denoised_tile.convertTo(denoised_tile, CV_16U);
denoised_tiles.push_back(denoised_tile);
// TODO: 4.2 Temporal Denoising
//goal: temporially denoise using the weiner filter
//input:
//1. array of 2D dft tiles of the reference image
//2. array of 2D dft tiles ocf the aligned alternate image
//3. estimated noise varaince
//4. temporal factor
//return: merged image patches dft
/*
//tile_size = TILE_SIZE;
double temporal_factor = 8 //8 by default
double temporal_noise_scaling = (pow(TILE_SIZE,2) * (1.0/16*2))*temporal_factor;
//start calculating the merged image tiles fft
for (int i = 0;i < burst_images.num_images; i++) {
if (i != burst_images.reference_image_idx) {
}
}
//sample of 0th image
altername_image = burst_images.bayer_images_pad[0]
//get the tiles of the alternate image
std::vector<cv::Mat> alternate_image_tiles = getReferenceTiles(channel_image);
//get the dft of the alternate image
std::vector<cv::Mat> alternate_tiles_DFT;
for (auto alt_tile : alternate_tiles_DFT) {
cv::Mat alt_tile_DFT;
alt_tile.convertTo(alt_tile_DFT, CV_32F);
cv::dft(alt_tile_DFT, alt_tile_DFT, cv::DFT_SCALE | cv::DFT_COMPLEX_OUTPUT);
alternate_tiles_DFT.push_back(alt_tile_DFT);
}
std::vector<cv::Mat> tile_differences = reference_tiles_DFT - alternate_tiles_DFT;
std::vector<cv::Mat> tile_sq_asolute_diff = tile_differences; //tile_differences.real**2 + tile_differnce.imag**2; //also tile_dist
std::vector<cv::Mat> A = tile_sq_asolute_diff/(tile_sq_asolute_diff+noise_variance)
std::vector<cv::Mat> merged_image_tiles_fft = alternate_tiles_DFT + A * tile_differences;
//use merged_image_tiles_fft into part 4.3
// TODO: 4.3 Spatial Denoising
// Apply IFFT on reference tiles (frequency to spatial)
std::vector<cv::Mat> denoised_tiles;
for (auto dft_tile : reference_tiles_DFT) {
cv::Mat denoised_tile;
cv::dft(dft_tile, denoised_tile, cv::DFT_INVERSE | cv::DFT_REAL_OUTPUT);
denoised_tile.convertTo(denoised_tile, CV_16U);
denoised_tiles.push_back(denoised_tile);
}
reference_tiles = denoised_tiles;
//adding after here
double spatial_factor = 1; //to be added
double spatial_noise_scaling = (pow(TILE_SIZE,2) * (1.0/16*2))*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;
merged_channel_tiles_spatial = reference_tiles*spatial_tile_dist/(spatial_tile_dist+WienerCoeff)
//apply the cosineWindow2D over the merged_channel_tiles_spatial and reconstruct the image
*/
// 4.4 Cosine Window Merging
// Process tiles through 2D cosine window
std::vector<cv::Mat> windowed_tiles;
for (auto tile : reference_tiles) {
windowed_tiles.push_back(cosineWindow2D(tile));
}
// Merge tiles
return mergeTiles(windowed_tiles, channel_image.rows, channel_image.cols);
}
reference_tiles = denoised_tiles;
// 4.4 Cosine Window Merging
// Process tiles through 2D cosine window
std::vector<cv::Mat> windowed_tiles;
for (auto tile : reference_tiles) {
windowed_tiles.push_back(cosineWindow2D(tile));
//Helper function to get the channels from the input image
std::vector<ushort> getChannels(cv::Mat input_image){
std::vector<ushort> channels[4];
for (int y = 0; y < input_image.rows; ++y) {
for (int x = 0; x < input_image.cols; ++x) {
if (y % 2 == 0) {
if (x % 2 == 0) {
channels[0].push_back(input_image.at<ushort>(y, x));
}
else {
channels[1].push_back(input_image.at<ushort>(y, x));
}
}
else {
if (x % 2 == 0) {
channels[2].push_back(input_image.at<ushort>(y, x));
}
else {
channels[3].push_back(input_image.at<ushort>(y, x));
}
}
}
}
return channels;
}
// Merge tiles
return mergeTiles(windowed_tiles, channel_image.rows, channel_image.cols);
}
//we should be getting the individual channel in the same place where we call the processChannel function with the reference channel in its arguments
} // namespace hdrplus
} // namespace hdrplus
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