Cycles: Implement denoising option for reducing noise in the rendered image

This commit contains the first part of the new Cycles denoising option,
which filters the resulting image using information gathered during rendering
to get rid of noise while preserving visual features as well as possible.

To use the option, enable it in the render layer options. The default settings
fit a wide range of scenes, but the user can tweak individual settings to
control the tradeoff between a noise-free image, image details, and calculation
time.

Note that the denoiser may still change in the future and that some features
are not implemented yet. The most important missing feature is animation
denoising, which uses information from multiple frames at once to produce a
flicker-free and smoother result. These features will be added in the future.

Finally, thanks to all the people who supported this project:

- Google (through the GSoC) and Theory Studios for sponsoring the development
- The authors of the papers I used for implementing the denoiser (more details
  on them will be included in the technical docs)
- The other Cycles devs for feedback on the code, especially Sergey for
  mentoring the GSoC project and Brecht for the code review!
- And of course the users who helped with testing, reported bugs and things
  that could and/or should work better!

1 files changed, 218 insertions, 0 deletions

diff --git a/intern/cycles/device/device_denoising.cpp b/intern/cycles/device/device_denoising.cpp
new file mode 100644
index 00000000000..39c8cf30105
--- /dev/null
+++ b/intern/cycles/device/device_denoising.cpp
@@ -0,0 +1,218 @@
+/*
+ * 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, 6,  7,  8, 12 };
+		int variance_from[] = { 3, 4, 5, 9, 10, 11, 13 };
+		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;
+		for(int pass = 0; pass < num_color_passes; pass++) {
+			device_sub_ptr color_pass    (device, buffer.mem,     mean_to[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);
+		}
+	}
+
+	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, *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