343 lines
13 KiB
C++
343 lines
13 KiB
C++
|
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||
|
|
//
|
||
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||
|
|
//
|
||
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
||
|
|
// If you do not agree to this license, do not download, install,
|
||
|
|
// copy or use the software.
|
||
|
|
//
|
||
|
|
//
|
||
|
|
// Intel License Agreement
|
||
|
|
// For Open Source Computer Vision Library
|
||
|
|
//
|
||
|
|
// Copyright (C) 2000, Intel Corporation, all rights reserved.
|
||
|
|
// Third party copyrights are property of their respective icvers.
|
||
|
|
//
|
||
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
||
|
|
// are permitted provided that the following conditions are met:
|
||
|
|
//
|
||
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
||
|
|
// this list of conditions and the following disclaimer.
|
||
|
|
//
|
||
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||
|
|
// this list of conditions and the following disclaimer in the documentation
|
||
|
|
// and/or other materials provided with the distribution.
|
||
|
|
//
|
||
|
|
// * The name of Intel Corporation may not be used to endorse or promote products
|
||
|
|
// derived from this software without specific prior written permission.
|
||
|
|
//
|
||
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
||
|
|
// any express or implied warranties, including, but not limited to, the implied
|
||
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
||
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
||
|
|
// loss of use, data, or profits; or business interruption) however caused
|
||
|
|
// and on any theory of liability, whether in contract, strict liability,
|
||
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
||
|
|
// the use of this software, even if advised of the possibility of such damage.
|
||
|
|
//
|
||
|
|
//M*/
|
||
|
|
|
||
|
|
#ifndef __OPENCV_FAST_NLMEANS_DENOISING_INVOKER_HPP__
|
||
|
|
#define __OPENCV_FAST_NLMEANS_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 FastNlMeansDenoisingInvoker {
|
||
|
|
public:
|
||
|
|
FastNlMeansDenoisingInvoker(const Mat& src, Mat& dst,
|
||
|
|
int template_window_size, int search_window_size, const double h);
|
||
|
|
|
||
|
|
void operator() (const BlockedRange& range) const;
|
||
|
|
|
||
|
|
private:
|
||
|
|
const Mat& src_;
|
||
|
|
Mat& dst_;
|
||
|
|
|
||
|
|
Mat extended_src_;
|
||
|
|
int border_size_;
|
||
|
|
|
||
|
|
int template_window_size_;
|
||
|
|
int search_window_size_;
|
||
|
|
|
||
|
|
int template_window_half_size_;
|
||
|
|
int search_window_half_size_;
|
||
|
|
|
||
|
|
int fixed_point_mult_;
|
||
|
|
int almost_template_window_size_sq_bin_shift;
|
||
|
|
vector<int> almost_dist2weight;
|
||
|
|
|
||
|
|
void calcDistSumsForFirstElementInRow(
|
||
|
|
int i,
|
||
|
|
Array2d<int>& dist_sums,
|
||
|
|
Array3d<int>& col_dist_sums,
|
||
|
|
Array3d<int>& up_col_dist_sums) const;
|
||
|
|
|
||
|
|
void calcDistSumsForElementInFirstRow(
|
||
|
|
int i,
|
||
|
|
int j,
|
||
|
|
int first_col_num,
|
||
|
|
Array2d<int>& dist_sums,
|
||
|
|
Array3d<int>& col_dist_sums,
|
||
|
|
Array3d<int>& up_col_dist_sums) const;
|
||
|
|
};
|
||
|
|
|
||
|
|
template <class T>
|
||
|
|
FastNlMeansDenoisingInvoker<T>::FastNlMeansDenoisingInvoker(
|
||
|
|
const cv::Mat& src,
|
||
|
|
cv::Mat& dst,
|
||
|
|
int template_window_size,
|
||
|
|
int search_window_size,
|
||
|
|
const double h) : src_(src), dst_(dst)
|
||
|
|
{
|
||
|
|
template_window_half_size_ = template_window_size / 2;
|
||
|
|
search_window_half_size_ = search_window_size / 2;
|
||
|
|
template_window_size_ = template_window_half_size_ * 2 + 1;
|
||
|
|
search_window_size_ = search_window_half_size_ * 2 + 1;
|
||
|
|
|
||
|
|
border_size_ = search_window_half_size_ + template_window_half_size_;
|
||
|
|
copyMakeBorder(src_, extended_src_,
|
||
|
|
border_size_, border_size_, border_size_, border_size_, cv::BORDER_DEFAULT);
|
||
|
|
|
||
|
|
const int max_estimate_sum_value = 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 * src_.channels();
|
||
|
|
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 * src_.channels())));
|
||
|
|
|
||
|
|
if (weight < WEIGHT_THRESHOLD * fixed_point_mult_) {
|
||
|
|
weight = 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
almost_dist2weight[almost_dist] = weight;
|
||
|
|
}
|
||
|
|
// additional optimization init end
|
||
|
|
|
||
|
|
if (dst_.empty()) {
|
||
|
|
dst_ = Mat::zeros(src_.size(), src_.type());
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
template <class T>
|
||
|
|
void FastNlMeansDenoisingInvoker<T>::operator() (const BlockedRange& range) const {
|
||
|
|
int row_from = range.begin();
|
||
|
|
int row_to = range.end() - 1;
|
||
|
|
|
||
|
|
int dist_sums_array[search_window_size_ * search_window_size_];
|
||
|
|
Array2d<int> dist_sums(dist_sums_array, search_window_size_, search_window_size_);
|
||
|
|
|
||
|
|
// for lazy calc optimization
|
||
|
|
int col_dist_sums_array[template_window_size_ * search_window_size_ * search_window_size_];
|
||
|
|
Array3d<int> col_dist_sums(&col_dist_sums_array[0],
|
||
|
|
template_window_size_, search_window_size_, search_window_size_);
|
||
|
|
|
||
|
|
int first_col_num = -1;
|
||
|
|
|
||
|
|
Array3d<int> up_col_dist_sums(src_.cols, search_window_size_, search_window_size_);
|
||
|
|
|
||
|
|
for (int i = row_from; i <= row_to; i++) {
|
||
|
|
for (int j = 0; j < src_.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 = extended_src_.at<T>(ay - template_window_half_size_ - 1, ax);
|
||
|
|
T a_down = 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 y = 0; y < search_window_size; y++) {
|
||
|
|
int* dist_sums_row = dist_sums.row_ptr(y);
|
||
|
|
|
||
|
|
int* col_dist_sums_row = col_dist_sums.row_ptr(first_col_num,y);
|
||
|
|
|
||
|
|
int* up_col_dist_sums_row = up_col_dist_sums.row_ptr(j, y);
|
||
|
|
|
||
|
|
const T* b_up_ptr =
|
||
|
|
extended_src_.ptr<T>(start_by - template_window_half_size_ - 1 + y);
|
||
|
|
|
||
|
|
const T* b_down_ptr =
|
||
|
|
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[src_.channels()];
|
||
|
|
for (int channel_num = 0; channel_num < src_.channels(); channel_num++) {
|
||
|
|
estimation[channel_num] = 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
for (int y = 0; y < search_window_size_; y++) {
|
||
|
|
const T* cur_row_ptr = extended_src_.ptr<T>(border_size_ + search_window_y + y);
|
||
|
|
int* dist_sums_row = dist_sums.row_ptr(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 < src_.channels(); channel_num++) {
|
||
|
|
estimation[channel_num] =
|
||
|
|
cvRound(((double)estimation[channel_num]) / weights_sum);
|
||
|
|
}
|
||
|
|
|
||
|
|
dst_.at<T>(i,j) = saturateCastFromArray<T>(estimation);
|
||
|
|
|
||
|
|
} else { // weights_sum == 0
|
||
|
|
dst_.at<T>(i,j) = src_.at<T>(i,j);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
template <class T>
|
||
|
|
inline void FastNlMeansDenoisingInvoker<T>::calcDistSumsForFirstElementInRow(
|
||
|
|
int i,
|
||
|
|
Array2d<int>& dist_sums,
|
||
|
|
Array3d<int>& col_dist_sums,
|
||
|
|
Array3d<int>& up_col_dist_sums) const
|
||
|
|
{
|
||
|
|
int j = 0;
|
||
|
|
|
||
|
|
for (int y = 0; y < search_window_size_; y++) {
|
||
|
|
for (int x = 0; x < search_window_size_; x++) {
|
||
|
|
dist_sums[y][x] = 0;
|
||
|
|
for (int tx = 0; tx < template_window_size_; tx++) {
|
||
|
|
col_dist_sums[tx][y][x] = 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
int start_y = i + y - search_window_half_size_;
|
||
|
|
int start_x = j + x - search_window_half_size_;
|
||
|
|
|
||
|
|
for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++) {
|
||
|
|
for (int tx = -template_window_half_size_; tx <= template_window_half_size_; tx++) {
|
||
|
|
int dist = calcDist<T>(extended_src_,
|
||
|
|
border_size_ + i + ty, border_size_ + j + tx,
|
||
|
|
border_size_ + start_y + ty, border_size_ + start_x + tx);
|
||
|
|
|
||
|
|
dist_sums[y][x] += dist;
|
||
|
|
col_dist_sums[tx + template_window_half_size_][y][x] += dist;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
up_col_dist_sums[j][y][x] = col_dist_sums[template_window_size_ - 1][y][x];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
template <class T>
|
||
|
|
inline void FastNlMeansDenoisingInvoker<T>::calcDistSumsForElementInFirstRow(
|
||
|
|
int i,
|
||
|
|
int j,
|
||
|
|
int first_col_num,
|
||
|
|
Array2d<int>& dist_sums,
|
||
|
|
Array3d<int>& col_dist_sums,
|
||
|
|
Array3d<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 y = 0; y < search_window_size_; y++) {
|
||
|
|
for (int x = 0; x < search_window_size_; x++) {
|
||
|
|
dist_sums[y][x] -= col_dist_sums[first_col_num][y][x];
|
||
|
|
|
||
|
|
col_dist_sums[new_last_col_num][y][x] = 0;
|
||
|
|
int by = start_by + y;
|
||
|
|
int bx = start_bx + x;
|
||
|
|
for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++) {
|
||
|
|
col_dist_sums[new_last_col_num][y][x] +=
|
||
|
|
calcDist<T>(extended_src_, ay + ty, ax, by + ty, bx);
|
||
|
|
}
|
||
|
|
|
||
|
|
dist_sums[y][x] += col_dist_sums[new_last_col_num][y][x];
|
||
|
|
|
||
|
|
up_col_dist_sums[j][y][x] = col_dist_sums[new_last_col_num][y][x];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
#endif
|