/* * 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. */ CCL_NAMESPACE_BEGIN ccl_device void kernel_filter_construct_transform(const float *ccl_restrict buffer, int x, int y, int4 rect, int pass_stride, float *transform, int *rank, int radius, float pca_threshold) { int buffer_w = align_up(rect.z - rect.x, 4); float features[DENOISE_FEATURES]; /* Temporary storage, used in different steps of the algorithm. */ float tempmatrix[DENOISE_FEATURES*DENOISE_FEATURES]; float tempvector[2*DENOISE_FEATURES]; const float *ccl_restrict pixel_buffer; int2 pixel; /* === Calculate denoising window. === */ int2 low = make_int2(max(rect.x, x - radius), max(rect.y, y - radius)); int2 high = make_int2(min(rect.z, x + radius + 1), min(rect.w, y + radius + 1)); int num_pixels = (high.y - low.y) * (high.x - low.x); /* === Shift feature passes to have mean 0. === */ float feature_means[DENOISE_FEATURES]; math_vector_zero(feature_means, DENOISE_FEATURES); FOR_PIXEL_WINDOW { filter_get_features(pixel, pixel_buffer, features, NULL, pass_stride); math_vector_add(feature_means, features, DENOISE_FEATURES); } END_FOR_PIXEL_WINDOW math_vector_scale(feature_means, 1.0f / num_pixels, DENOISE_FEATURES); /* === Scale the shifted feature passes to a range of [-1; 1], will be baked into the transform later. === */ float *feature_scale = tempvector; math_vector_zero(feature_scale, DENOISE_FEATURES); FOR_PIXEL_WINDOW { filter_get_feature_scales(pixel, pixel_buffer, features, feature_means, pass_stride); math_vector_max(feature_scale, features, DENOISE_FEATURES); } END_FOR_PIXEL_WINDOW filter_calculate_scale(feature_scale); /* === Generate the feature transformation. === * This transformation maps the DENOISE_FEATURES-dimentional feature space to a reduced feature (r-feature) space * which generally has fewer dimensions. This mainly helps to prevent overfitting. */ float* feature_matrix = tempmatrix; math_matrix_zero(feature_matrix, DENOISE_FEATURES); FOR_PIXEL_WINDOW { filter_get_features(pixel, pixel_buffer, features, feature_means, pass_stride); math_vector_mul(features, feature_scale, DENOISE_FEATURES); math_matrix_add_gramian(feature_matrix, DENOISE_FEATURES, features, 1.0f); } END_FOR_PIXEL_WINDOW math_matrix_jacobi_eigendecomposition(feature_matrix, transform, DENOISE_FEATURES, 1); *rank = 0; /* Prevent overfitting when a small window is used. */ int max_rank = min(DENOISE_FEATURES, num_pixels/3); if(pca_threshold < 0.0f) { float threshold_energy = 0.0f; for(int i = 0; i < DENOISE_FEATURES; i++) { threshold_energy += feature_matrix[i*DENOISE_FEATURES+i]; } threshold_energy *= 1.0f - (-pca_threshold); float reduced_energy = 0.0f; for(int i = 0; i < max_rank; i++, (*rank)++) { if(i >= 2 && reduced_energy >= threshold_energy) break; float s = feature_matrix[i*DENOISE_FEATURES+i]; reduced_energy += s; } } else { for(int i = 0; i < max_rank; i++, (*rank)++) { float s = feature_matrix[i*DENOISE_FEATURES+i]; if(i >= 2 && sqrtf(s) < pca_threshold) break; } } /* Bake the feature scaling into the transformation matrix. */ for(int i = 0; i < (*rank); i++) { math_vector_mul(transform + i*DENOISE_FEATURES, feature_scale, DENOISE_FEATURES); } math_matrix_transpose(transform, DENOISE_FEATURES, 1); } CCL_NAMESPACE_END