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+// Copyright (c) the JPEG XL Project Authors. All rights reserved.
+//
+// Use of this source code is governed by a BSD-style
+// license that can be found in the LICENSE file.
+
+#ifndef LIB_JXL_GAUSS_BLUR_H_
+#define LIB_JXL_GAUSS_BLUR_H_
+
+#include <stddef.h>
+
+#include <cmath>
+#include <hwy/aligned_allocator.h>
+#include <vector>
+
+#include "lib/jxl/base/data_parallel.h"
+#include "lib/jxl/base/status.h"
+#include "lib/jxl/image.h"
+
+namespace jxl {
+
+template <typename T>
+std::vector<T> GaussianKernel(int radius, T sigma) {
+  JXL_ASSERT(sigma > 0.0);
+  std::vector<T> kernel(2 * radius + 1);
+  const T scaler = -1.0 / (2 * sigma * sigma);
+  double sum = 0.0;
+  for (int i = -radius; i <= radius; ++i) {
+    const T val = std::exp(scaler * i * i);
+    kernel[i + radius] = val;
+    sum += val;
+  }
+  for (size_t i = 0; i < kernel.size(); ++i) {
+    kernel[i] /= sum;
+  }
+  return kernel;
+}
+
+// All convolution functions below apply mirroring of the input on the borders
+// in the following way:
+//
+//     input: [a0 a1 a2 ...  aN]
+//     mirrored input: [aR ... a1 | a0 a1 a2 .... aN | aN-1 ... aN-R]
+//
+// where R is the radius of the kernel (i.e. kernel size is 2*R+1).
+
+// REQUIRES: in.xsize() and in.ysize() are integer multiples of res.
+ImageF ConvolveAndSample(const ImageF& in, const std::vector<float>& kernel,
+                         const size_t res);
+
+// Private, used by test.
+void ExtrapolateBorders(const float* const JXL_RESTRICT row_in,
+                        float* const JXL_RESTRICT row_out, const int xsize,
+                        const int radius);
+
+// Only for use by CreateRecursiveGaussian and FastGaussian*.
+#pragma pack(push, 1)
+struct RecursiveGaussian {
+  // For k={1,3,5} in that order, each broadcasted 4x for LoadDup128. Used only
+  // for vertical passes.
+  float n2[3 * 4];
+  float d1[3 * 4];
+
+  // We unroll horizontal passes 4x - one output per lane. These are each lane's
+  // multiplier for the previous output (relative to the first of the four
+  // outputs). Indexing: 4 * 0..2 (for {1,3,5}) + 0..3 for the lane index.
+  float mul_prev[3 * 4];
+  // Ditto for the second to last output.
+  float mul_prev2[3 * 4];
+
+  // We multiply a vector of inputs 0..3 by a vector shifted from this array.
+  // in=0 uses all 4 (nonzero) terms; for in=3, the lower three lanes are 0.
+  float mul_in[3 * 4];
+
+  size_t radius;
+};
+#pragma pack(pop)
+
+// Precomputation for FastGaussian*; users may use the same pointer/storage in
+// subsequent calls to FastGaussian* with the same sigma.
+hwy::AlignedUniquePtr<RecursiveGaussian> CreateRecursiveGaussian(double sigma);
+
+// 1D Gaussian with zero-pad boundary handling and runtime independent of sigma.
+void FastGaussian1D(const hwy::AlignedUniquePtr<RecursiveGaussian>& rg,
+                    const float* JXL_RESTRICT in, intptr_t width,
+                    float* JXL_RESTRICT out);
+
+// 2D Gaussian with zero-pad boundary handling and runtime independent of sigma.
+void FastGaussian(const hwy::AlignedUniquePtr<RecursiveGaussian>& rg,
+                  const ImageF& in, ThreadPool* pool, ImageF* JXL_RESTRICT temp,
+                  ImageF* JXL_RESTRICT out);
+
+}  // namespace jxl
+
+#endif  // LIB_JXL_GAUSS_BLUR_H_