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388 lines
15 KiB
C++
388 lines
15 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective icvers.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef __OPENCV_FAST_NLMEANS_MULTI_DENOISING_INVOKER_HPP__
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#define __OPENCV_FAST_NLMEANS_MULTI_DENOISING_INVOKER_HPP__
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#include "precomp.hpp"
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#include <opencv2/core/core.hpp>
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#include <opencv2/core/internal.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#include <limits>
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#include "fast_nlmeans_denoising_invoker_commons.hpp"
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#include "arrays.hpp"
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using namespace std;
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using namespace cv;
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template <typename T>
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struct FastNlMeansMultiDenoisingInvoker {
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public:
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FastNlMeansMultiDenoisingInvoker(
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const std::vector<Mat>& srcImgs, int imgToDenoiseIndex, int temporalWindowSize,
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Mat& dst, int template_window_size, int search_window_size, const float h);
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void operator() (const BlockedRange& range) const;
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private:
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void operator= (const FastNlMeansMultiDenoisingInvoker&);
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int rows_;
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int cols_;
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Mat& dst_;
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vector<Mat> extended_srcs_;
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Mat main_extended_src_;
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int border_size_;
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int template_window_size_;
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int search_window_size_;
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int temporal_window_size_;
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int template_window_half_size_;
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int search_window_half_size_;
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int temporal_window_half_size_;
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int fixed_point_mult_;
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int almost_template_window_size_sq_bin_shift;
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vector<int> almost_dist2weight;
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void calcDistSumsForFirstElementInRow(
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int i,
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Array3d<int>& dist_sums,
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Array4d<int>& col_dist_sums,
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Array4d<int>& up_col_dist_sums) const;
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void calcDistSumsForElementInFirstRow(
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int i,
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int j,
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int first_col_num,
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Array3d<int>& dist_sums,
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Array4d<int>& col_dist_sums,
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Array4d<int>& up_col_dist_sums) const;
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};
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template <class T>
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FastNlMeansMultiDenoisingInvoker<T>::FastNlMeansMultiDenoisingInvoker(
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const vector<Mat>& srcImgs,
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int imgToDenoiseIndex,
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int temporalWindowSize,
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cv::Mat& dst,
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int template_window_size,
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int search_window_size,
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const float h) : dst_(dst), extended_srcs_(srcImgs.size())
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{
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CV_Assert(srcImgs.size() > 0);
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CV_Assert(srcImgs[0].channels() == sizeof(T));
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rows_ = srcImgs[0].rows;
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cols_ = srcImgs[0].cols;
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template_window_half_size_ = template_window_size / 2;
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search_window_half_size_ = search_window_size / 2;
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temporal_window_half_size_ = temporalWindowSize / 2;
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template_window_size_ = template_window_half_size_ * 2 + 1;
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search_window_size_ = search_window_half_size_ * 2 + 1;
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temporal_window_size_ = temporal_window_half_size_ * 2 + 1;
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border_size_ = search_window_half_size_ + template_window_half_size_;
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for (int i = 0; i < temporal_window_size_; i++) {
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copyMakeBorder(
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srcImgs[imgToDenoiseIndex - temporal_window_half_size_ + i], extended_srcs_[i],
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border_size_, border_size_, border_size_, border_size_, cv::BORDER_DEFAULT);
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}
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main_extended_src_ = extended_srcs_[temporal_window_half_size_];
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const int max_estimate_sum_value =
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temporal_window_size_ * search_window_size_ * search_window_size_ * 255;
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fixed_point_mult_ = numeric_limits<int>::max() / max_estimate_sum_value;
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// precalc weight for every possible l2 dist between blocks
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// additional optimization of precalced weights to replace division(averaging) by binary shift
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int template_window_size_sq = template_window_size_ * template_window_size_;
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almost_template_window_size_sq_bin_shift = 0;
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while (1 << almost_template_window_size_sq_bin_shift < template_window_size_sq) {
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almost_template_window_size_sq_bin_shift++;
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}
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int almost_template_window_size_sq = 1 << almost_template_window_size_sq_bin_shift;
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double almost_dist2actual_dist_multiplier =
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((double) almost_template_window_size_sq) / template_window_size_sq;
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int max_dist = 255 * 255 * sizeof(T);
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int almost_max_dist = (int) (max_dist / almost_dist2actual_dist_multiplier + 1);
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almost_dist2weight.resize(almost_max_dist);
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const double WEIGHT_THRESHOLD = 0.001;
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for (int almost_dist = 0; almost_dist < almost_max_dist; almost_dist++) {
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double dist = almost_dist * almost_dist2actual_dist_multiplier;
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int weight = cvRound(fixed_point_mult_ * std::exp(-dist / (h * h * sizeof(T))));
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if (weight < WEIGHT_THRESHOLD * fixed_point_mult_) {
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weight = 0;
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}
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almost_dist2weight[almost_dist] = weight;
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}
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CV_Assert(almost_dist2weight[0] == fixed_point_mult_);
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// additional optimization init end
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if (dst_.empty()) {
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dst_ = Mat::zeros(srcImgs[0].size(), srcImgs[0].type());
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}
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}
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template <class T>
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void FastNlMeansMultiDenoisingInvoker<T>::operator() (const BlockedRange& range) const {
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int row_from = range.begin();
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int row_to = range.end() - 1;
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Array3d<int> dist_sums(temporal_window_size_, search_window_size_, search_window_size_);
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// for lazy calc optimization
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Array4d<int> col_dist_sums(
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template_window_size_, temporal_window_size_, search_window_size_, search_window_size_);
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int first_col_num = -1;
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Array4d<int> up_col_dist_sums(
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cols_, temporal_window_size_, search_window_size_, search_window_size_);
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for (int i = row_from; i <= row_to; i++) {
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for (int j = 0; j < cols_; j++) {
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int search_window_y = i - search_window_half_size_;
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int search_window_x = j - search_window_half_size_;
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// calc dist_sums
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if (j == 0) {
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calcDistSumsForFirstElementInRow(i, dist_sums, col_dist_sums, up_col_dist_sums);
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first_col_num = 0;
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} else { // calc cur dist_sums using previous dist_sums
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if (i == row_from) {
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calcDistSumsForElementInFirstRow(i, j, first_col_num,
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dist_sums, col_dist_sums, up_col_dist_sums);
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} else {
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int ay = border_size_ + i;
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int ax = border_size_ + j + template_window_half_size_;
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int start_by =
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border_size_ + i - search_window_half_size_;
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int start_bx =
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border_size_ + j - search_window_half_size_ + template_window_half_size_;
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T a_up = main_extended_src_.at<T>(ay - template_window_half_size_ - 1, ax);
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T a_down = main_extended_src_.at<T>(ay + template_window_half_size_, ax);
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// copy class member to local variable for optimization
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int search_window_size = search_window_size_;
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for (int d = 0; d < temporal_window_size_; d++) {
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Mat cur_extended_src = extended_srcs_[d];
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Array2d<int> cur_dist_sums = dist_sums[d];
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Array2d<int> cur_col_dist_sums = col_dist_sums[first_col_num][d];
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Array2d<int> cur_up_col_dist_sums = up_col_dist_sums[j][d];
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for (int y = 0; y < search_window_size; y++) {
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int* dist_sums_row = cur_dist_sums.row_ptr(y);
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int* col_dist_sums_row = cur_col_dist_sums.row_ptr(y);
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int* up_col_dist_sums_row = cur_up_col_dist_sums.row_ptr(y);
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const T* b_up_ptr =
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cur_extended_src.ptr<T>(start_by - template_window_half_size_ - 1 + y);
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const T* b_down_ptr =
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cur_extended_src.ptr<T>(start_by + template_window_half_size_ + y);
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for (int x = 0; x < search_window_size; x++) {
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dist_sums_row[x] -= col_dist_sums_row[x];
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col_dist_sums_row[x] = up_col_dist_sums_row[x] +
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calcUpDownDist(
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a_up, a_down,
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b_up_ptr[start_bx + x], b_down_ptr[start_bx + x]
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);
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dist_sums_row[x] += col_dist_sums_row[x];
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up_col_dist_sums_row[x] = col_dist_sums_row[x];
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}
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}
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}
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}
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first_col_num = (first_col_num + 1) % template_window_size_;
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}
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// calc weights
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int weights_sum = 0;
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int estimation[3];
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for (size_t channel_num = 0; channel_num < sizeof(T); channel_num++) {
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estimation[channel_num] = 0;
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}
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for (int d = 0; d < temporal_window_size_; d++) {
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const Mat& esrc_d = extended_srcs_[d];
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for (int y = 0; y < search_window_size_; y++) {
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const T* cur_row_ptr = esrc_d.ptr<T>(border_size_ + search_window_y + y);
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int* dist_sums_row = dist_sums.row_ptr(d, y);
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for (int x = 0; x < search_window_size_; x++) {
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int almostAvgDist =
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dist_sums_row[x] >> almost_template_window_size_sq_bin_shift;
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int weight = almost_dist2weight[almostAvgDist];
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weights_sum += weight;
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T p = cur_row_ptr[border_size_ + search_window_x + x];
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incWithWeight(estimation, weight, p);
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}
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}
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}
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for (size_t channel_num = 0; channel_num < sizeof(T); channel_num++)
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estimation[channel_num] = (estimation[channel_num] + weights_sum / 2) / weights_sum;
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dst_.at<T>(i,j) = saturateCastFromArray<T>(estimation);
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}
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}
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}
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template <class T>
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inline void FastNlMeansMultiDenoisingInvoker<T>::calcDistSumsForFirstElementInRow(
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int i,
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Array3d<int>& dist_sums,
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Array4d<int>& col_dist_sums,
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Array4d<int>& up_col_dist_sums) const
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{
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int j = 0;
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for (int d = 0; d < temporal_window_size_; d++) {
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Mat cur_extended_src = extended_srcs_[d];
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for (int y = 0; y < search_window_size_; y++) {
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for (int x = 0; x < search_window_size_; x++) {
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dist_sums[d][y][x] = 0;
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for (int tx = 0; tx < template_window_size_; tx++) {
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col_dist_sums[tx][d][y][x] = 0;
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}
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int start_y = i + y - search_window_half_size_;
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int start_x = j + x - search_window_half_size_;
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int* dist_sums_ptr = &dist_sums[d][y][x];
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int* col_dist_sums_ptr = &col_dist_sums[0][d][y][x];
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int col_dist_sums_step = col_dist_sums.step_size(0);
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for (int tx = -template_window_half_size_; tx <= template_window_half_size_; tx++) {
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for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++) {
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int dist = calcDist<T>(
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main_extended_src_.at<T>(
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border_size_ + i + ty, border_size_ + j + tx),
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cur_extended_src.at<T>(
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border_size_ + start_y + ty, border_size_ + start_x + tx)
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);
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*dist_sums_ptr += dist;
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*col_dist_sums_ptr += dist;
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}
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col_dist_sums_ptr += col_dist_sums_step;
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}
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up_col_dist_sums[j][d][y][x] = col_dist_sums[template_window_size_ - 1][d][y][x];
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}
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}
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}
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}
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template <class T>
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inline void FastNlMeansMultiDenoisingInvoker<T>::calcDistSumsForElementInFirstRow(
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int i,
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int j,
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int first_col_num,
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Array3d<int>& dist_sums,
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Array4d<int>& col_dist_sums,
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Array4d<int>& up_col_dist_sums) const
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{
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int ay = border_size_ + i;
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int ax = border_size_ + j + template_window_half_size_;
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int start_by = border_size_ + i - search_window_half_size_;
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int start_bx = border_size_ + j - search_window_half_size_ + template_window_half_size_;
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int new_last_col_num = first_col_num;
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for (int d = 0; d < temporal_window_size_; d++) {
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Mat cur_extended_src = extended_srcs_[d];
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for (int y = 0; y < search_window_size_; y++) {
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for (int x = 0; x < search_window_size_; x++) {
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dist_sums[d][y][x] -= col_dist_sums[first_col_num][d][y][x];
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col_dist_sums[new_last_col_num][d][y][x] = 0;
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int by = start_by + y;
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int bx = start_bx + x;
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int* col_dist_sums_ptr = &col_dist_sums[new_last_col_num][d][y][x];
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for (int ty = -template_window_half_size_; ty <= template_window_half_size_; ty++) {
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*col_dist_sums_ptr +=
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calcDist<T>(
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main_extended_src_.at<T>(ay + ty, ax),
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cur_extended_src.at<T>(by + ty, bx)
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);
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}
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dist_sums[d][y][x] += col_dist_sums[new_last_col_num][d][y][x];
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up_col_dist_sums[j][d][y][x] = col_dist_sums[new_last_col_num][d][y][x];
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}
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}
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}
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}
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#endif
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