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avfilter/vf_vaguedenoiser: add new type of threshold

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This commit is contained in:
Paul B Mahol 2020-06-07 15:10:03 +02:00
parent 6c57b0d63a
commit bd6336b970
2 changed files with 61 additions and 5 deletions
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14
doc/filters.texi
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@ -19476,6 +19476,20 @@ Partial of full denoising (limited coefficients shrinking), from 0 to 100. Defau
@item planes
A list of the planes to process. By default all planes are processed.
@item type
The threshold type the filter will use.
It accepts the following values:
@table @samp
@item universal
Threshold used is same for all decompositions.
@item bayes
Threshold used depends also on each decomposition coefficients.
@end table
Default is universal.
@end table
@section vectorscope

52
libavfilter/vf_vaguedenoiser.c
View File

@ -38,6 +38,7 @@ typedef struct VagueDenoiserContext {
float threshold;
float percent;
int method;
int type;
int nsteps;
int planes;
@ -60,7 +61,7 @@ typedef struct VagueDenoiserContext {
void (*thresholding)(float *block, const int width, const int height,
const int stride, const float threshold,
const float percent, const int nsteps);
const float percent);
} VagueDenoiserContext;
#define OFFSET(x) offsetof(VagueDenoiserContext, x)
@ -74,6 +75,9 @@ static const AVOption vaguedenoiser_options[] = {
{ "nsteps", "set number of steps", OFFSET(nsteps), AV_OPT_TYPE_INT, {.i64=6 }, 1, 32, FLAGS },
{ "percent", "set percent of full denoising", OFFSET(percent),AV_OPT_TYPE_FLOAT, {.dbl=85}, 0,100, FLAGS },
{ "planes", "set planes to filter", OFFSET(planes), AV_OPT_TYPE_INT, {.i64=15 }, 0, 15, FLAGS },
{ "type", "set threshold type", OFFSET(type), AV_OPT_TYPE_INT, {.i64=0 }, 0, 1, FLAGS, "type" },
{ "universal", "universal (VisuShrink)", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "type" },
{ "bayes", "bayes (BayesShrink)", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "type" },
{ NULL }
};
@ -333,7 +337,7 @@ static void invert_step(const float *input, float *output, float *temp, const in
static void hard_thresholding(float *block, const int width, const int height,
const int stride, const float threshold,
const float percent, const int unused)
const float percent)
{
const float frac = 1.f - percent * 0.01f;
int y, x;
@ -348,7 +352,7 @@ static void hard_thresholding(float *block, const int width, const int height,
}
static void soft_thresholding(float *block, const int width, const int height, const int stride,
const float threshold, const float percent, const int nsteps)
const float threshold, const float percent)
{
const float frac = 1.f - percent * 0.01f;
const float shift = threshold * 0.01f * percent;
@ -368,7 +372,7 @@ static void soft_thresholding(float *block, const int width, const int height, c
static void qian_thresholding(float *block, const int width, const int height,
const int stride, const float threshold,
const float percent, const int unused)
const float percent)
{
const float percent01 = percent * 0.01f;
const float tr2 = threshold * threshold * percent01;
@ -389,6 +393,23 @@ static void qian_thresholding(float *block, const int width, const int height,
}
}
static float bayes_threshold(float *block, const int width, const int height,
const int stride, const float threshold)
{
float mean = 0.f;
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++) {
mean += block[x] * block[x];
}
block += stride;
}
mean /= width * height;
return threshold * threshold / (FFMAX(sqrtf(mean - threshold), FLT_EPSILON));
}
static void filter(VagueDenoiserContext *s, AVFrame *in, AVFrame *out)
{
int p, y, x, i, j;
@ -452,7 +473,28 @@ static void filter(VagueDenoiserContext *s, AVFrame *in, AVFrame *out)
v_low_size0 = (v_low_size0 + 1) >> 1;
}
s->thresholding(s->block, width, height, width, s->threshold, s->percent, s->nsteps);
if (s->type == 0) {
s->thresholding(s->block, width, height, width, s->threshold, s->percent);
} else {
for (int n = 0; n < s->nsteps; n++) {
float threshold;
float *block;
if (n == s->nsteps - 1) {
threshold = bayes_threshold(s->block, s->hlowsize[p][n], s->vlowsize[p][n], width, s->threshold);
s->thresholding(s->block, s->hlowsize[p][n], s->vlowsize[p][n], width, threshold, s->percent);
}
block = s->block + s->hlowsize[p][n];
threshold = bayes_threshold(block, s->hhighsize[p][n], s->vlowsize[p][n], width, s->threshold);
s->thresholding(block, s->hhighsize[p][n], s->vlowsize[p][n], width, threshold, s->percent);
block = s->block + s->vlowsize[p][n] * width;
threshold = bayes_threshold(block, s->hlowsize[p][n], s->vhighsize[p][n], width, s->threshold);
s->thresholding(block, s->hlowsize[p][n], s->vhighsize[p][n], width, threshold, s->percent);
block = s->block + s->hlowsize[p][n] + s->vlowsize[p][n] * width;
threshold = bayes_threshold(block, s->hhighsize[p][n], s->vhighsize[p][n], width, s->threshold);
s->thresholding(block, s->hhighsize[p][n], s->vhighsize[p][n], width, threshold, s->percent);
}
}
while (nsteps_invert--) {
const int idx = s->vlowsize[p][nsteps_invert] + s->vhighsize[p][nsteps_invert];