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Using WEIGHT_THRESHOLD to limit table size. Still problematic with 16-bit and big h-values.

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This commit is contained in:
Erik Karlsson 2015-02-13 00:11:30 +01:00
parent 42db9e7153
commit d588c717da
2 changed files with 29 additions and 30 deletions
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30
modules/photo/src/fast_nlmeans_denoising_invoker.hpp
View File

@ -123,31 +123,28 @@ FastNlMeansDenoisingInvoker<T, IT, UIT>::FastNlMeansDenoisingInvoker(
// precalc weight for every possible l2 dist between blocks
// additional optimization of precalced weights to replace division(averaging) by binary shift
// squared distances are truncated to 24 bits to avoid unreasonable table sizes
// TODO: uses lots of memory and loses precision wtih 16-bit images ????
const size_t TABLE_MAX_BITS = 24;
CV_Assert(template_window_size_ <= 46340); // sqrt(INT_MAX)
int template_window_size_sq = template_window_size_ * template_window_size_;
almost_template_window_size_sq_bin_shift_ = getNearestPowerOf2(template_window_size_sq) +
std::max(2*pixelInfo<T>::sampleBits(), TABLE_MAX_BITS) - TABLE_MAX_BITS;
almost_template_window_size_sq_bin_shift_ = getNearestPowerOf2(template_window_size_sq);
double almost_dist2actual_dist_multiplier = ((double)(1 << almost_template_window_size_sq_bin_shift_)) / template_window_size_sq;
const double WEIGHT_THRESHOLD = 0.001;
const size_t ALLOC_CHUNK = 65536;
IT max_dist =
(IT)pixelInfo<T>::sampleMax() * (IT)pixelInfo<T>::sampleMax() * (IT)pixelInfo<T>::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++)
int almost_max_dist = 0;
while (true)
{
double dist = almost_dist * almost_dist2actual_dist_multiplier;
double dist = almost_max_dist * almost_dist2actual_dist_multiplier;
IT weight = (IT)round(fixed_point_mult_ * std::exp(-dist / (h * h * pixelInfo<T>::channels)));
if (weight < WEIGHT_THRESHOLD * fixed_point_mult_ || dist > max_dist) break;
if (weight < WEIGHT_THRESHOLD * fixed_point_mult_)
weight = 0;
if (almost_max_dist >= almost_dist2weight_.size())
almost_dist2weight_.resize(almost_max_dist + ALLOC_CHUNK);
almost_dist2weight_[almost_dist] = weight;
almost_dist2weight_[almost_max_dist++] = weight;
}
almost_dist2weight_.resize(almost_max_dist);
CV_Assert(almost_dist2weight_[0] == fixed_point_mult_);
// additional optimization init end
@ -161,6 +158,8 @@ void FastNlMeansDenoisingInvoker<T, IT, UIT>::operator() (const Range& range) co
int row_from = range.start;
int row_to = range.end - 1;
int almost_max_dist = almost_dist2weight_.size();
// sums of cols anf rows for current pixel p
Array2d<IT> dist_sums(search_window_size_, search_window_size_);
@ -244,7 +243,8 @@ void FastNlMeansDenoisingInvoker<T, IT, UIT>::operator() (const Range& range) co
for (int x = 0; x < search_window_size_; x++)
{
int almostAvgDist = (int)(dist_sums_row[x] >> almost_template_window_size_sq_bin_shift_);
IT weight = almost_dist2weight_[almostAvgDist];
IT weight =
almostAvgDist < almost_max_dist ? almost_dist2weight_[almostAvgDist] : 0;
weights_sum += weight;
T p = cur_row_ptr[border_size_ + search_window_x + x];

29
modules/photo/src/fast_nlmeans_multi_denoising_invoker.hpp
View File

@ -131,35 +131,31 @@ FastNlMeansMultiDenoisingInvoker<T, IT, UIT>::FastNlMeansMultiDenoisingInvoker(
// precalc weight for every possible l2 dist between blocks
// additional optimization of precalced weights to replace division(averaging) by binary shift
// squared distances are truncated to 24 bits to avoid unreasonable table sizes
// TODO: uses lots of memory and loses precision wtih 16-bit images ????
const size_t TABLE_MAX_BITS = 24;
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++;
almost_template_window_size_sq_bin_shift +=
std::max(2*pixelInfo<T>::sampleBits(), TABLE_MAX_BITS) - TABLE_MAX_BITS;
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;
const double WEIGHT_THRESHOLD = 0.001;
const size_t ALLOC_CHUNK = 65536;
IT max_dist =
(IT)pixelInfo<T>::sampleMax() * (IT)pixelInfo<T>::sampleMax() * (IT)pixelInfo<T>::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++)
int almost_max_dist = 0;
while (true)
{
double dist = almost_dist * almost_dist2actual_dist_multiplier;
double dist = almost_max_dist * almost_dist2actual_dist_multiplier;
IT weight = (IT)round(fixed_point_mult_ * std::exp(-dist / (h * h * pixelInfo<T>::channels)));
if (weight < WEIGHT_THRESHOLD * fixed_point_mult_ || dist > max_dist) break;
if (weight < WEIGHT_THRESHOLD * fixed_point_mult_)
weight = 0;
if (almost_max_dist >= almost_dist2weight.size())
almost_dist2weight.resize(almost_max_dist + ALLOC_CHUNK);
almost_dist2weight[almost_dist] = weight;
almost_dist2weight[almost_max_dist++] = weight;
}
almost_dist2weight.resize(almost_max_dist);
CV_Assert(almost_dist2weight[0] == fixed_point_mult_);
// additional optimization init end
@ -173,6 +169,8 @@ void FastNlMeansMultiDenoisingInvoker<T, IT, UIT>::operator() (const Range& rang
int row_from = range.start;
int row_to = range.end - 1;
int almost_max_dist = almost_dist2weight.size();
Array3d<IT> dist_sums(temporal_window_size_, search_window_size_, search_window_size_);
// for lazy calc optimization
@ -273,7 +271,8 @@ void FastNlMeansMultiDenoisingInvoker<T, IT, UIT>::operator() (const Range& rang
{
int almostAvgDist = (int)(dist_sums_row[x] >> almost_template_window_size_sq_bin_shift);
IT weight = almost_dist2weight[almostAvgDist];
IT weight =
almostAvgDist < almost_max_dist ? almost_dist2weight[almostAvgDist] : 0;
weights_sum += weight;
T p = cur_row_ptr[border_size_ + search_window_x + x];