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blender/intern/cycles/device/device_denoising.cpp
Lukas Stockner 705c43be0b Cycles Denoising: Merge outlier heuristic and confidence interval test 
The previous outlier heuristic only checked whether the pixel is more than
twice as bright compared to the 75% quantile of the 5x5 neighborhood.
While this detected fireflies robustly, it also incorrectly marked a lot of
legitimate small highlights as outliers and filtered them away.

This commit adds an additional condition for marking a pixel as a firefly:
In addition to being above the reference brightness, the lower end of the
3-sigma confidence interval has to be below it.
Since the lower end approximates how low the true value of the pixel might be,
this test separates pixels that are supposed to be very bright from pixels that
are very bright due to random fireflies.

Also, since there is now a reliable outlier filter as a preprocessing step,
the additional confidence interval test in the reconstruction kernel is no
longer needed.
2017-06-09 03:46:11 +02:00

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/*
* Copyright 2011-2017 Blender Foundation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "device/device_denoising.h"
#include "kernel/filter/filter_defines.h"
CCL_NAMESPACE_BEGIN
void DenoisingTask::init_from_devicetask(const DeviceTask &task)
{
radius = task.denoising_radius;
nlm_k_2 = powf(2.0f, lerp(-5.0f, 3.0f, task.denoising_strength));
if(task.denoising_relative_pca) {
pca_threshold = -powf(10.0f, lerp(-8.0f, 0.0f, task.denoising_feature_strength));
}
else {
pca_threshold = powf(10.0f, lerp(-5.0f, 3.0f, task.denoising_feature_strength));
}
render_buffer.pass_stride = task.pass_stride;
render_buffer.denoising_data_offset = task.pass_denoising_data;
render_buffer.denoising_clean_offset = task.pass_denoising_clean;
/* Expand filter_area by radius pixels and clamp the result to the extent of the neighboring tiles */
rect = make_int4(max(tiles->x[0], filter_area.x - radius),
max(tiles->y[0], filter_area.y - radius),
min(tiles->x[3], filter_area.x + filter_area.z + radius),
min(tiles->y[3], filter_area.y + filter_area.w + radius));
}
void DenoisingTask::tiles_from_rendertiles(RenderTile *rtiles)
{
tiles = (TilesInfo*) tiles_mem.resize(sizeof(TilesInfo)/sizeof(int));
device_ptr buffers[9];
for(int i = 0; i < 9; i++) {
buffers[i] = rtiles[i].buffer;
tiles->offsets[i] = rtiles[i].offset;
tiles->strides[i] = rtiles[i].stride;
}
tiles->x[0] = rtiles[3].x;
tiles->x[1] = rtiles[4].x;
tiles->x[2] = rtiles[5].x;
tiles->x[3] = rtiles[5].x + rtiles[5].w;
tiles->y[0] = rtiles[1].y;
tiles->y[1] = rtiles[4].y;
tiles->y[2] = rtiles[7].y;
tiles->y[3] = rtiles[7].y + rtiles[7].h;
render_buffer.offset = rtiles[4].offset;
render_buffer.stride = rtiles[4].stride;
render_buffer.ptr = rtiles[4].buffer;
functions.set_tiles(buffers);
}
bool DenoisingTask::run_denoising()
{
/* Allocate denoising buffer. */
buffer.passes = 14;
buffer.w = align_up(rect.z - rect.x, 4);
buffer.h = rect.w - rect.y;
buffer.pass_stride = align_up(buffer.w * buffer.h, divide_up(device->mem_address_alignment(), sizeof(float)));
buffer.mem.resize(buffer.pass_stride * buffer.passes);
device->mem_alloc("Denoising Pixel Buffer", buffer.mem, MEM_READ_WRITE);
device_ptr null_ptr = (device_ptr) 0;
/* Prefilter shadow feature. */
{
device_sub_ptr unfiltered_a (device, buffer.mem, 0, buffer.pass_stride, MEM_READ_WRITE);
device_sub_ptr unfiltered_b (device, buffer.mem, 1*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
device_sub_ptr sample_var (device, buffer.mem, 2*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
device_sub_ptr sample_var_var (device, buffer.mem, 3*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
device_sub_ptr buffer_var (device, buffer.mem, 5*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
device_sub_ptr filtered_var (device, buffer.mem, 6*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
device_sub_ptr nlm_temporary_1(device, buffer.mem, 7*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
device_sub_ptr nlm_temporary_2(device, buffer.mem, 8*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
device_sub_ptr nlm_temporary_3(device, buffer.mem, 9*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
nlm_state.temporary_1_ptr = *nlm_temporary_1;
nlm_state.temporary_2_ptr = *nlm_temporary_2;
nlm_state.temporary_3_ptr = *nlm_temporary_3;
/* Get the A/B unfiltered passes, the combined sample variance, the estimated variance of the sample variance and the buffer variance. */
functions.divide_shadow(*unfiltered_a, *unfiltered_b, *sample_var, *sample_var_var, *buffer_var);
/* Smooth the (generally pretty noisy) buffer variance using the spatial information from the sample variance. */
nlm_state.set_parameters(6, 3, 4.0f, 1.0f);
functions.non_local_means(*buffer_var, *sample_var, *sample_var_var, *filtered_var);
/* Reuse memory, the previous data isn't needed anymore. */
device_ptr filtered_a = *buffer_var,
filtered_b = *sample_var;
/* Use the smoothed variance to filter the two shadow half images using each other for weight calculation. */
nlm_state.set_parameters(5, 3, 1.0f, 0.25f);
functions.non_local_means(*unfiltered_a, *unfiltered_b, *filtered_var, filtered_a);
functions.non_local_means(*unfiltered_b, *unfiltered_a, *filtered_var, filtered_b);
device_ptr residual_var = *sample_var_var;
/* Estimate the residual variance between the two filtered halves. */
functions.combine_halves(filtered_a, filtered_b, null_ptr, residual_var, 2, rect);
device_ptr final_a = *unfiltered_a,
final_b = *unfiltered_b;
/* Use the residual variance for a second filter pass. */
nlm_state.set_parameters(4, 2, 1.0f, 0.5f);
functions.non_local_means(filtered_a, filtered_b, residual_var, final_a);
functions.non_local_means(filtered_b, filtered_a, residual_var, final_b);
/* Combine the two double-filtered halves to a final shadow feature. */
device_sub_ptr shadow_pass(device, buffer.mem, 4*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
functions.combine_halves(final_a, final_b, *shadow_pass, null_ptr, 0, rect);
}
/* Prefilter general features. */
{
device_sub_ptr unfiltered (device, buffer.mem, 8*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
device_sub_ptr variance (device, buffer.mem, 9*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
device_sub_ptr nlm_temporary_1(device, buffer.mem, 10*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
device_sub_ptr nlm_temporary_2(device, buffer.mem, 11*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
device_sub_ptr nlm_temporary_3(device, buffer.mem, 12*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
nlm_state.temporary_1_ptr = *nlm_temporary_1;
nlm_state.temporary_2_ptr = *nlm_temporary_2;
nlm_state.temporary_3_ptr = *nlm_temporary_3;
int mean_from[] = { 0, 1, 2, 12, 6, 7, 8 };
int variance_from[] = { 3, 4, 5, 13, 9, 10, 11};
int pass_to[] = { 1, 2, 3, 0, 5, 6, 7};
for(int pass = 0; pass < 7; pass++) {
device_sub_ptr feature_pass(device, buffer.mem, pass_to[pass]*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
/* Get the unfiltered pass and its variance from the RenderBuffers. */
functions.get_feature(mean_from[pass], variance_from[pass], *unfiltered, *variance);
/* Smooth the pass and store the result in the denoising buffers. */
nlm_state.set_parameters(2, 2, 1.0f, 0.25f);
functions.non_local_means(*unfiltered, *unfiltered, *variance, *feature_pass);
}
}
/* Copy color passes. */
{
int mean_from[] = {20, 21, 22};
int variance_from[] = {23, 24, 25};
int mean_to[] = { 8, 9, 10};
int variance_to[] = {11, 12, 13};
int num_color_passes = 3;
device_only_memory<float> temp_color;
temp_color.resize(3*buffer.pass_stride);
device->mem_alloc("Denoising temporary color", temp_color, MEM_READ_WRITE);
for(int pass = 0; pass < num_color_passes; pass++) {
device_sub_ptr color_pass(device, temp_color, pass*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
device_sub_ptr color_var_pass(device, buffer.mem, variance_to[pass]*buffer.pass_stride, buffer.pass_stride, MEM_READ_WRITE);
functions.get_feature(mean_from[pass], variance_from[pass], *color_pass, *color_var_pass);
}
{
device_sub_ptr depth_pass (device, buffer.mem, 0, buffer.pass_stride, MEM_READ_WRITE);
device_sub_ptr color_var_pass(device, buffer.mem, variance_to[0]*buffer.pass_stride, 3*buffer.pass_stride, MEM_READ_WRITE);
device_sub_ptr output_pass (device, buffer.mem, mean_to[0]*buffer.pass_stride, 3*buffer.pass_stride, MEM_READ_WRITE);
functions.detect_outliers(temp_color.device_pointer, *color_var_pass, *depth_pass, *output_pass);
}
device->mem_free(temp_color);
}
storage.w = filter_area.z;
storage.h = filter_area.w;
storage.transform.resize(storage.w*storage.h*TRANSFORM_SIZE);
storage.rank.resize(storage.w*storage.h);
device->mem_alloc("Denoising Transform", storage.transform, MEM_READ_WRITE);
device->mem_alloc("Denoising Rank", storage.rank, MEM_READ_WRITE);
functions.construct_transform();
device_only_memory<float> temporary_1;
device_only_memory<float> temporary_2;
temporary_1.resize(buffer.w*buffer.h);
temporary_2.resize(buffer.w*buffer.h);
device->mem_alloc("Denoising NLM temporary 1", temporary_1, MEM_READ_WRITE);
device->mem_alloc("Denoising NLM temporary 2", temporary_2, MEM_READ_WRITE);
reconstruction_state.temporary_1_ptr = temporary_1.device_pointer;
reconstruction_state.temporary_2_ptr = temporary_2.device_pointer;
storage.XtWX.resize(storage.w*storage.h*XTWX_SIZE);
storage.XtWY.resize(storage.w*storage.h*XTWY_SIZE);
device->mem_alloc("Denoising XtWX", storage.XtWX, MEM_READ_WRITE);
device->mem_alloc("Denoising XtWY", storage.XtWY, MEM_READ_WRITE);
reconstruction_state.filter_rect = make_int4(filter_area.x-rect.x, filter_area.y-rect.y, storage.w, storage.h);
int tile_coordinate_offset = filter_area.y*render_buffer.stride + filter_area.x;
reconstruction_state.buffer_params = make_int4(render_buffer.offset + tile_coordinate_offset,
render_buffer.stride,
render_buffer.pass_stride,
render_buffer.denoising_clean_offset);
reconstruction_state.source_w = rect.z-rect.x;
reconstruction_state.source_h = rect.w-rect.y;
{
device_sub_ptr color_ptr (device, buffer.mem, 8*buffer.pass_stride, 3*buffer.pass_stride, MEM_READ_WRITE);
device_sub_ptr color_var_ptr(device, buffer.mem, 11*buffer.pass_stride, 3*buffer.pass_stride, MEM_READ_WRITE);
functions.reconstruct(*color_ptr, *color_var_ptr, render_buffer.ptr);
}
device->mem_free(storage.XtWX);
device->mem_free(storage.XtWY);
device->mem_free(storage.transform);
device->mem_free(storage.rank);
device->mem_free(temporary_1);
device->mem_free(temporary_2);
device->mem_free(buffer.mem);
device->mem_free(tiles_mem);
return true;
}
CCL_NAMESPACE_END
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