opencv/modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp

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#ifndef __OPENCV_FAST_NLMEANS_MULTI_DENOISING_INVOKER_HPP__
#define __OPENCV_FAST_NLMEANS_MULTI_DENOISING_INVOKER_HPP__
#include "precomp.hpp"
#include <opencv2/core/core.hpp>
#include <opencv2/core/internal.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <limits>
#include "fast_nlmeans_denoising_invoker_commons.hpp"
#include "arrays.hpp"
using namespace std;
using namespace cv;
template <typename T>
struct FastNlMeansMultiDenoisingInvoker {
public:
FastNlMeansMultiDenoisingInvoker(
const std::vector<Mat>& srcImgs, int imgToDenoiseIndex, int temporalWindowSize,
Mat& dst, int template_window_size, int search_window_size, const double h);
void operator() (const BlockedRange& range) const;
void operator= (const FastNlMeansMultiDenoisingInvoker&) {
CV_Error(CV_StsNotImplemented, "Assigment operator is not implemented");
}
private:
int rows_;
int cols_;
int channels_count_;
Mat& dst_;
vector<Mat> extended_srcs_;
Mat main_extended_src_;
int border_size_;
int template_window_size_;
int search_window_size_;
int temporal_window_size_;
int template_window_half_size_;
int search_window_half_size_;
int temporal_window_half_size_;
int fixed_point_mult_;
int almost_template_window_size_sq_bin_shift;
vector<int> almost_dist2weight;
void calcDistSumsForFirstElementInRow(
int i,
Array3d<int>& dist_sums,
Array4d<int>& col_dist_sums,
Array4d<int>& up_col_dist_sums) const;
void calcDistSumsForElementInFirstRow(
int i,
int j,
int first_col_num,
Array3d<int>& dist_sums,
Array4d<int>& col_dist_sums,
Array4d<int>& up_col_dist_sums) const;
};
template <class T>
FastNlMeansMultiDenoisingInvoker<T>::FastNlMeansMultiDenoisingInvoker(
const vector<Mat>& srcImgs,
int imgToDenoiseIndex,
int temporalWindowSize,
cv::Mat& dst,
int template_window_size,
int search_window_size,
const double h) : dst_(dst), extended_srcs_(srcImgs.size())
{
2012-08-21 20:05:18 +08:00
CV_Assert(srcImgs.size() > 0);
CV_Assert(srcImgs[0].channels() <= 3);
rows_ = srcImgs[0].rows;
cols_ = srcImgs[0].cols;
channels_count_ = srcImgs[0].channels();
template_window_half_size_ = template_window_size / 2;
search_window_half_size_ = search_window_size / 2;
temporal_window_half_size_ = temporalWindowSize / 2;
template_window_size_ = template_window_half_size_ * 2 + 1;
search_window_size_ = search_window_half_size_ * 2 + 1;
temporal_window_size_ = temporal_window_half_size_ * 2 + 1;
border_size_ = search_window_half_size_ + template_window_half_size_;
for (int i = 0; i < temporal_window_size_; i++) {
copyMakeBorder(
srcImgs[imgToDenoiseIndex - temporal_window_half_size_ + i], extended_srcs_[i],
border_size_, border_size_, border_size_, border_size_, cv::BORDER_DEFAULT);
}
main_extended_src_ = extended_srcs_[temporal_window_half_size_];
const int max_estimate_sum_value =
temporal_window_size_ * search_window_size_ * search_window_size_ * 255;
fixed_point_mult_ = numeric_limits<int>::max() / max_estimate_sum_value;
// precalc weight for every possible l2 dist between blocks
// additional optimization of precalced weights to replace division(averaging) by binary shift
int template_window_size_sq = template_window_size_ * template_window_size_;
almost_template_window_size_sq_bin_shift = 0;
while (1 << almost_template_window_size_sq_bin_shift < template_window_size_sq) {
almost_template_window_size_sq_bin_shift++;
}
int almost_template_window_size_sq = 1 << almost_template_window_size_sq_bin_shift;
double almost_dist2actual_dist_multiplier =
((double) almost_template_window_size_sq) / template_window_size_sq;
int max_dist = 256 * 256 * channels_count_;
int almost_max_dist = (int) (max_dist / almost_dist2actual_dist_multiplier + 1);
almost_dist2weight.resize(almost_max_dist);
const double WEIGHT_THRESHOLD = 0.001;
for (int almost_dist = 0; almost_dist < almost_max_dist; almost_dist++) {
double dist = almost_dist * almost_dist2actual_dist_multiplier;
int weight = cvRound(fixed_point_mult_ * std::exp(- dist / (h * h * channels_count_)));
if (weight < WEIGHT_THRESHOLD * fixed_point_mult_) {
weight = 0;
}
almost_dist2weight[almost_dist] = weight;
}
// additional optimization init end
if (dst_.empty()) {
dst_ = Mat::zeros(srcImgs[0].size(), srcImgs[0].type());
}
}
template <class T>
void FastNlMeansMultiDenoisingInvoker<T>::operator() (const BlockedRange& range) const {
int row_from = range.begin();
int row_to = range.end() - 1;
Array3d<int> dist_sums(temporal_window_size_, search_window_size_, search_window_size_);
// for lazy calc optimization
Array4d<int> col_dist_sums(
template_window_size_, temporal_window_size_, search_window_size_, search_window_size_);
int first_col_num = -1;
Array4d<int> up_col_dist_sums(
cols_, temporal_window_size_, search_window_size_, search_window_size_);
for (int i = row_from; i <= row_to; i++) {
for (int j = 0; j < cols_; j++) {
int search_window_y = i - search_window_half_size_;
int search_window_x = j - search_window_half_size_;
// calc dist_sums
if (j == 0) {
calcDistSumsForFirstElementInRow(i, dist_sums, col_dist_sums, up_col_dist_sums);
first_col_num = 0;
} else { // calc cur dist_sums using previous dist_sums
if (i == row_from) {
calcDistSumsForElementInFirstRow(i, j, first_col_num,
dist_sums, col_dist_sums, up_col_dist_sums);
} else {
int ay = border_size_ + i;
int ax = border_size_ + j + template_window_half_size_;
int start_by =
border_size_ + i - search_window_half_size_;
int start_bx =
border_size_ + j - search_window_half_size_ + template_window_half_size_;
T a_up = main_extended_src_.at<T>(ay - template_window_half_size_ - 1, ax);
T a_down = main_extended_src_.at<T>(ay + template_window_half_size_, ax);
// copy class member to local variable for optimization
int search_window_size = search_window_size_;
for (int d = 0; d < temporal_window_size_; d++) {
Mat cur_extended_src = extended_srcs_[d];
Array2d<int> cur_dist_sums = dist_sums[d];
Array2d<int> cur_col_dist_sums = col_dist_sums[first_col_num][d];
Array2d<int> cur_up_col_dist_sums = up_col_dist_sums[j][d];
for (int y = 0; y < search_window_size; y++) {
int* dist_sums_row = cur_dist_sums.row_ptr(y);
int* col_dist_sums_row = cur_col_dist_sums.row_ptr(y);
int* up_col_dist_sums_row = cur_up_col_dist_sums.row_ptr(y);
const T* b_up_ptr =
cur_extended_src.ptr<T>(start_by - template_window_half_size_ - 1 + y);
const T* b_down_ptr =
cur_extended_src.ptr<T>(start_by + template_window_half_size_ + y);
for (int x = 0; x < search_window_size; x++) {
dist_sums_row[x] -= col_dist_sums_row[x];
col_dist_sums_row[x] = up_col_dist_sums_row[x] +
calcUpDownDist(
a_up, a_down,
b_up_ptr[start_bx + x], b_down_ptr[start_bx + x]
);
dist_sums_row[x] += col_dist_sums_row[x];
up_col_dist_sums_row[x] = col_dist_sums_row[x];
}
}
}
}
first_col_num = (first_col_num + 1) % template_window_size_;
}
// calc weights
int weights_sum = 0;
int estimation[3];
for (int channel_num = 0; channel_num < channels_count_; channel_num++) {
estimation[channel_num] = 0;
}
for (int d = 0; d < temporal_window_size_; d++) {
const Mat& esrc_d = extended_srcs_[d];
for (int y = 0; y < search_window_size_; y++) {
const T* cur_row_ptr = esrc_d.ptr<T>(border_size_ + search_window_y + y);
int* dist_sums_row = dist_sums.row_ptr(d, y);
for (int x = 0; x < search_window_size_; x++) {
int almostAvgDist =
dist_sums_row[x] >> almost_template_window_size_sq_bin_shift;
int weight = almost_dist2weight[almostAvgDist];
weights_sum += weight;
T p = cur_row_ptr[border_size_ + search_window_x + x];
incWithWeight(estimation, weight, p);
}
}
}
if (weights_sum > 0) {
for (int channel_num = 0; channel_num < channels_count_; channel_num++)
estimation[channel_num] = (estimation[channel_num] + weights_sum / 2) / weights_sum;
dst_.at<T>(i,j) = saturateCastFromArray<T>(estimation);
} else { // weights_sum == 0
const Mat& esrc = extended_srcs_[temporal_window_half_size_];
dst_.at<T>(i,j) = esrc.at<T>(i,j);
}
}
}
}
template <class T>
inline void FastNlMeansMultiDenoisingInvoker<T>::calcDistSumsForFirstElementInRow(
int i,
Array3d<int>& dist_sums,
Array4d<int>& col_dist_sums,
Array4d<int>& up_col_dist_sums) const
{
int j = 0;
for (int d = 0; d < temporal_window_size_; d++) {
Mat cur_extended_src = extended_srcs_[d];
for (int y = 0; y < search_window_size_; y++) {
for (int x = 0; x < search_window_size_; x++) {
dist_sums[d][y][x] = 0;
for (int tx = 0; tx < template_window_size_; tx++) {
col_dist_sums[tx][d][y][x] = 0;
}
int start_y = i + y - search_window_half_size_;
int start_x = j + x - search_window_half_size_;
int* dist_sums_ptr = &dist_sums[d][y][x];
int* col_dist_sums_ptr = &col_dist_sums[0][d][y][x];
int col_dist_sums_step = col_dist_sums.step_size(0);
for (int tx = -template_window_half_size_; tx <= template_window_half_size_; tx++) {
for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++) {
int dist = calcDist<T>(
main_extended_src_.at<T>(
border_size_ + i + ty, border_size_ + j + tx),
cur_extended_src.at<T>(
border_size_ + start_y + ty, border_size_ + start_x + tx)
);
*dist_sums_ptr += dist;
*col_dist_sums_ptr += dist;
}
col_dist_sums_ptr += col_dist_sums_step;
}
up_col_dist_sums[j][d][y][x] = col_dist_sums[template_window_size_ - 1][d][y][x];
}
}
}
}
template <class T>
inline void FastNlMeansMultiDenoisingInvoker<T>::calcDistSumsForElementInFirstRow(
int i,
int j,
int first_col_num,
Array3d<int>& dist_sums,
Array4d<int>& col_dist_sums,
Array4d<int>& up_col_dist_sums) const
{
int ay = border_size_ + i;
int ax = border_size_ + j + template_window_half_size_;
int start_by = border_size_ + i - search_window_half_size_;
int start_bx = border_size_ + j - search_window_half_size_ + template_window_half_size_;
int new_last_col_num = first_col_num;
for (int d = 0; d < temporal_window_size_; d++) {
Mat cur_extended_src = extended_srcs_[d];
for (int y = 0; y < search_window_size_; y++) {
for (int x = 0; x < search_window_size_; x++) {
dist_sums[d][y][x] -= col_dist_sums[first_col_num][d][y][x];
col_dist_sums[new_last_col_num][d][y][x] = 0;
int by = start_by + y;
int bx = start_bx + x;
int* col_dist_sums_ptr = &col_dist_sums[new_last_col_num][d][y][x];
for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++) {
*col_dist_sums_ptr +=
calcDist<T>(
main_extended_src_.at<T>(ay + ty, ax),
cur_extended_src.at<T>(by + ty, bx)
);
}
dist_sums[d][y][x] += col_dist_sums[new_last_col_num][d][y][x];
up_col_dist_sums[j][d][y][x] = col_dist_sums[new_last_col_num][d][y][x];
}
}
}
}
#endif