cacorrectrgb.c

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/*
    This file is part of darktable,
    Copyright (C) 2020-2021 rawfiner.
    Copyright (C) 2021, 2023, 2025-2026 Aurélien PIERRE.
    Copyright (C) 2021 luzpaz.
    Copyright (C) 2021-2022 Pascal Obry.
    Copyright (C) 2021 Ralf Brown.
    Copyright (C) 2022 Diederik Ter Rahe.
    Copyright (C) 2022 Martin Bařinka.
    Copyright (C) 2022 Philipp Lutz.
    Copyright (C) 2022 Sakari Kapanen.
    
    darktable is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.
    
    darktable is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.
    
    You should have received a copy of the GNU General Public License
    along with darktable.  If not, see <http://www.gnu.org/licenses/>.
*/
 
#ifdef HAVE_CONFIG_H
#include "config.h"
#endif
 
#include "bauhaus/bauhaus.h"
#include "develop/imageop.h"
#include "develop/imageop_gui.h"
#include "gui/color_picker_proxy.h"
#include "gui/gtk.h"
#include "iop/iop_api.h"
#include "common/gaussian.h"
#include "common/fast_guided_filter.h"
 
#include <gtk/gtk.h>
#include <stdlib.h>
 
DT_MODULE_INTROSPECTION(1, dt_iop_cacorrectrgb_params_t)
 
 
typedef enum dt_iop_cacorrectrgb_guide_channel_t
{
  DT_CACORRECT_RGB_R = 0,    // $DESCRIPTION: "red"
  DT_CACORRECT_RGB_G = 1,    // $DESCRIPTION: "green"
  DT_CACORRECT_RGB_B = 2     // $DESCRIPTION: "blue"
} dt_iop_cacorrectrgb_guide_channel_t;
 
typedef enum dt_iop_cacorrectrgb_mode_t
{
  DT_CACORRECT_MODE_STANDARD = 0,  // $DESCRIPTION: "standard"
  DT_CACORRECT_MODE_DARKEN = 1,    // $DESCRIPTION: "darken only"
  DT_CACORRECT_MODE_BRIGHTEN = 2   // $DESCRIPTION: "brighten only"
} dt_iop_cacorrectrgb_mode_t;
 
typedef struct dt_iop_cacorrectrgb_params_t
{
  dt_iop_cacorrectrgb_guide_channel_t guide_channel; // $DEFAULT: DT_CACORRECT_RGB_G $DESCRIPTION: "guide"
  float radius; // $MIN: 1 $MAX: 500 $DEFAULT: 5 $DESCRIPTION: "radius"
  float strength; // $MIN: 0 $MAX: 4 $DEFAULT: 0.5 $DESCRIPTION: "strength"
  dt_iop_cacorrectrgb_mode_t mode; // $DEFAULT: DT_CACORRECT_MODE_STANDARD $DESCRIPTION: "correction mode"
  gboolean refine_manifolds; // $MIN: FALSE $MAX: TRUE $DEFAULT: FALSE $DESCRIPTION: "very large chromatic aberration"
} dt_iop_cacorrectrgb_params_t;
 
typedef struct dt_iop_cacorrectrgb_gui_data_t
{
  GtkWidget *guide_channel, *radius, *strength, *mode, *refine_manifolds;
} dt_iop_cacorrectrgb_gui_data_t;
 
const char *name()
{
  return _("chromatic a_berrations");
}
 
const char **description(struct dt_iop_module_t *self)
{
  return dt_iop_set_description(self, _("correct chromatic aberrations"),
                                      _("corrective"),
                                      _("linear, raw, scene-referred"),
                                      _("linear, raw"),
                                      _("linear, raw, scene-referred"));
}
 
int flags()
{
  return IOP_FLAGS_INCLUDE_IN_STYLES | IOP_FLAGS_SUPPORTS_BLENDING;
}
 
int default_group()
{
  return IOP_GROUP_REPAIR;
}
 
int default_colorspace(dt_iop_module_t *self, dt_dev_pixelpipe_t *pipe, const dt_dev_pixelpipe_iop_t *piece)
{
  return IOP_CS_RGB;
}
 
void commit_params(dt_iop_module_t *self, dt_iop_params_t *p1, dt_dev_pixelpipe_t *pipe, dt_dev_pixelpipe_iop_t *piece)
{
  memcpy(piece->data, p1, self->params_size);
}
 
__DT_CLONE_TARGETS__
static void normalize_manifolds(const float *const restrict blurred_in, float *const restrict blurred_manifold_lower, float *const restrict blurred_manifold_higher, const size_t width, const size_t height, const dt_iop_cacorrectrgb_guide_channel_t guide)
{
  __OMP_PARALLEL_FOR__()
  for(size_t k = 0; k < width * height; k++)
  {
    const float weighth = fmaxf(blurred_manifold_higher[k * 4 + 3], 1E-2f);
    const float weightl = fmaxf(blurred_manifold_lower[k * 4 + 3], 1E-2f);
 
    // normalize guide
    const float highg = blurred_manifold_higher[k * 4 + guide] / weighth;
    const float lowg = blurred_manifold_lower[k * 4 + guide] / weightl;
 
    blurred_manifold_higher[k * 4 + guide] = highg;
    blurred_manifold_lower[k * 4 + guide] = lowg;
 
    // normalize and unlog other channels
    for(size_t kc = 0; kc <= 1; kc++)
    {
      const size_t c = (kc + guide + 1) % 3;
      const float highc = blurred_manifold_higher[k * 4 + c] / weighth;
      const float lowc = blurred_manifold_lower[k * 4 + c] / weightl;
      blurred_manifold_higher[k * 4 + c] = exp2f(highc) * highg;
      blurred_manifold_lower[k * 4 + c] = exp2f(lowc) * lowg;
    }
 
    // replace by average if weight is too small
    if(weighth < 0.05f)
    {
      // we make a smooth transition between full manifold at
      // weighth = 0.05f to full average at weighth = 0.01f
      const float w = (weighth - 0.01f) / (0.05f - 0.01f);
      for_each_channel(c,aligned(blurred_manifold_higher,blurred_in))
      {
        blurred_manifold_higher[k * 4 + c] = w * blurred_manifold_higher[k * 4 + c]
                                           + (1.0f - w) * blurred_in[k * 4 + c];
      }
    }
    if(weightl < 0.05f)
    {
      // we make a smooth transition between full manifold at
      // weightl = 0.05f to full average at weightl = 0.01f
      const float w = (weightl - 0.01f) / (0.05f - 0.01f);
      for_each_channel(c,aligned(blurred_manifold_lower,blurred_in))
      {
        blurred_manifold_lower[k * 4 + c] = w * blurred_manifold_lower[k * 4 + c]
                                          + (1.0f - w) * blurred_in[k * 4 + c];
      }
    }
  }
}
 
#define DT_CACORRECTRGB_MAX_EV_DIFF 2.0f
__DT_CLONE_TARGETS__
static int get_manifolds(const float* const restrict in, const size_t width, const size_t height,
                         const float sigma, const float sigma2,
                         const dt_iop_cacorrectrgb_guide_channel_t guide,
                         float* const restrict manifolds, gboolean refine_manifolds)
{
  int err = 0;
  float *const restrict blurred_in = dt_pixelpipe_cache_alloc_align_float_cache(width * height * 4, 0);
  float *const restrict manifold_higher = dt_pixelpipe_cache_alloc_align_float_cache(width * height * 4, 0);
  float *const restrict manifold_lower = dt_pixelpipe_cache_alloc_align_float_cache(width * height * 4, 0);
  float *const restrict blurred_manifold_higher = dt_pixelpipe_cache_alloc_align_float_cache(width * height * 4, 0);
  float *const restrict blurred_manifold_lower = dt_pixelpipe_cache_alloc_align_float_cache(width * height * 4, 0);
 
  if(IS_NULL_PTR(blurred_in) || IS_NULL_PTR(manifold_higher) || IS_NULL_PTR(manifold_lower) ||
     IS_NULL_PTR(blurred_manifold_higher) || IS_NULL_PTR(blurred_manifold_lower))
  {
    err = 1;
    goto error;
  }
 
  dt_aligned_pixel_t max = {INFINITY, INFINITY, INFINITY, INFINITY};
  dt_aligned_pixel_t min = {-INFINITY, -INFINITY, -INFINITY, 0.0f};
  // start with a larger blur to estimate the manifolds if we refine them
  // later on
  const float blur_size = refine_manifolds ? sigma2 : sigma;
  dt_gaussian_t *g = dt_gaussian_init(width, height, 4, max, min, blur_size, 0);
  if(IS_NULL_PTR(g))
  {
    err = 1;
    goto error;
  }
  dt_gaussian_blur_4c(g, in, blurred_in);
 
  // construct the manifolds
  // higher manifold is the blur of all pixels that are above average,
  // lower manifold is the blur of all pixels that are below average
  // we use the guide channel to categorize the pixels as above or below average
  __OMP_PARALLEL_FOR__()
  for(size_t k = 0; k < width * height; k++)
  {
    const float pixelg = fmaxf(in[k * 4 + guide], 1E-6f);
    const float avg = blurred_in[k * 4 + guide];
    float weighth = (pixelg >= avg);
    float weightl = (pixelg <= avg);
    float logdiffs[2];
    for(size_t kc = 0; kc <= 1; kc++)
    {
      const size_t c = (kc + guide + 1) % 3;
      const float pixel = fmaxf(in[k * 4 + c], 1E-6f);
      const float log_diff = log2f(pixel / pixelg);
      logdiffs[kc] = log_diff;
    }
    // regularization of logdiff to avoid too many problems with noise:
    // we lower the weights of pixels with too high logdiff
    const float maxlogdiff = fmaxf(fabsf(logdiffs[0]), fabsf(logdiffs[1]));
    if(maxlogdiff > DT_CACORRECTRGB_MAX_EV_DIFF)
    {
      const float correction_weight = DT_CACORRECTRGB_MAX_EV_DIFF / maxlogdiff;
      weightl *= correction_weight;
      weighth *= correction_weight;
    }
    for(size_t kc = 0; kc <= 1; kc++)
    {
      const size_t c = (kc + guide + 1) % 3;
      manifold_higher[k * 4 + c] = logdiffs[kc] * weighth;
      manifold_lower[k * 4 + c] = logdiffs[kc] * weightl;
    }
    manifold_higher[k * 4 + guide] = pixelg * weighth;
    manifold_lower[k * 4 + guide] = pixelg * weightl;
    manifold_higher[k * 4 + 3] = weighth;
    manifold_lower[k * 4 + 3] = weightl;
  }
 
  dt_gaussian_blur_4c(g, manifold_higher, blurred_manifold_higher);
  dt_gaussian_blur_4c(g, manifold_lower, blurred_manifold_lower);
  dt_gaussian_free(g);
 
  normalize_manifolds(blurred_in, blurred_manifold_lower, blurred_manifold_higher, width, height, guide);
 
  // note that manifolds were constructed based on the value and average
  // of the guide channel ONLY.
  // this implies that the "higher" manifold in the channel c may be
  // actually lower than the "lower" manifold of that channel.
  // This happens in the following example:
  // guide:  1_____
  //               |_____0
  // guided:        _____1
  //         0_____|
  // here the higher manifold of guide is equal to 1, its lower manifold is
  // equal to 0. The higher manifold of the guided channel is equal to 0
  // as it is the average of the values where the guide is higher than its
  // average, and the lower manifold of the guided channel is equal to 1.
 
  if(refine_manifolds)
  {
    g = dt_gaussian_init(width, height, 4, max, min, sigma, 0);
    if(IS_NULL_PTR(g))
    {
      err = 1;
      goto error;
    }
    dt_gaussian_blur_4c(g, in, blurred_in);
 
    // refine the manifolds
    // improve result especially on very degraded images
    // we use a blur of normal size for this step
    __OMP_PARALLEL_FOR__()
    for(size_t k = 0; k < width * height; k++)
    {
      // in order to refine the manifolds, we will compute weights
      // for which all channels will have a contribution.
      // this will allow to avoid taking too much into account pixels
      // that have wrong values due to the chromatic aberration
      //
      // for example, here:
      // guide:  1_____
      //               |_____0
      // guided: 1______
      //                |____0
      //               ^ this pixel makes the estimated lower manifold erroneous
      // here, the higher and lower manifolds values computed are:
      // _______|_higher_|________lower_________|
      // guide  |    1   |   0                  |
      // guided |    1   |(1 + 4 * 0) / 5 = 0.2 |
      //
      // the lower manifold of the guided is 0.2 if we consider only the guide
      //
      // at this step of the algorithm, we know estimates of manifolds
      //
      // we can refine the manifolds by computing weights that reduce the influence
      // of pixels that are probably suffering from chromatic aberrations
      const float pixelg = log2f(fmaxf(in[k * 4 + guide], 1E-6f));
      const float highg = log2f(fmaxf(blurred_manifold_higher[k * 4 + guide], 1E-6f));
      const float lowg = log2f(fmaxf(blurred_manifold_lower[k * 4 + guide], 1E-6f));
      const float avgg = log2f(fmaxf(blurred_in[k * 4 + guide], 1E-6f));
 
      float w = 1.0f;
      for(size_t kc = 0; kc <= 1; kc++)
      {
        const size_t c = (guide + kc + 1) % 3;
        // weight by considering how close pixel is for a manifold,
        // and how close the log difference between the channels is
        // close to the wrong log difference between the channels.
 
        const float pixel = log2f(fmaxf(in[k * 4 + c], 1E-6f));
        const float highc = log2f(fmaxf(blurred_manifold_higher[k * 4 + c], 1E-6f));
        const float lowc = log2f(fmaxf(blurred_manifold_lower[k * 4 + c], 1E-6f));
 
        // find how likely the pixel is part of a chromatic aberration
        // (lowc, lowg) and (highc, highg) are valid points
        // (lowc, highg) and (highc, lowg) are chromatic aberrations
        const float dist_to_ll = fabsf(pixelg - lowg - pixel + lowc);
        const float dist_to_hh = fabsf(pixelg - highg - pixel + highc);
        const float dist_to_lh = fabsf((pixelg - pixel) - (highg - lowc));
        const float dist_to_hl = fabsf((pixelg - pixel) - (lowg - highc));
 
        float dist_to_good = 1.0f;
        if(fabsf(pixelg - lowg) < fabsf(pixelg - highg))
          dist_to_good = dist_to_ll;
        else
          dist_to_good = dist_to_hh;
 
        float dist_to_bad = 1.0f;
        if(fabsf(pixelg - lowg) < fabsf(pixelg - highg))
          dist_to_bad = dist_to_hl;
        else
          dist_to_bad = dist_to_lh;
 
        // make w higher if close to good, and smaller if close to bad.
        w *= 1.0f * (0.2f + 1.0f / fmaxf(dist_to_good, 0.1f)) / (0.2f + 1.0f / fmaxf(dist_to_bad, 0.1f));
      }
 
      if(pixelg > avgg)
      {
        float logdiffs[2];
        for(size_t kc = 0; kc <= 1; kc++)
        {
          const size_t c = (guide + kc + 1) % 3;
          const float pixel = fmaxf(in[k * 4 + c], 1E-6f);
          const float log_diff = log2f(pixel) - pixelg;
          logdiffs[kc] = log_diff;
        }
        // regularization of logdiff to avoid too many problems with noise:
        // we lower the weights of pixels with too high logdiff
        const float maxlogdiff = fmaxf(fabsf(logdiffs[0]), fabsf(logdiffs[1]));
        if(maxlogdiff > DT_CACORRECTRGB_MAX_EV_DIFF)
        {
          const float correction_weight = DT_CACORRECTRGB_MAX_EV_DIFF / maxlogdiff;
          w *= correction_weight;
        }
        for(size_t kc = 0; kc <= 1; kc++)
        {
          const size_t c = (kc + guide + 1) % 3;
          manifold_higher[k * 4 + c] = logdiffs[kc] * w;
        }
        manifold_higher[k * 4 + guide] = fmaxf(in[k * 4 + guide], 0.0f) * w;
        manifold_higher[k * 4 + 3] = w;
        // manifold_lower still contains the values from first iteration
        // -> reset it.
        for_four_channels(c)
        {
          manifold_lower[k * 4 + c] = 0.0f;
        }
      }
      else
      {
        float logdiffs[2];
        for(size_t kc = 0; kc <= 1; kc++)
        {
          const size_t c = (guide + kc + 1) % 3;
          const float pixel = fmaxf(in[k * 4 + c], 1E-6f);
          const float log_diff = log2f(pixel) - pixelg;
          logdiffs[kc] = log_diff;
        }
        // regularization of logdiff to avoid too many problems with noise:
        // we lower the weights of pixels with too high logdiff
        const float maxlogdiff = fmaxf(fabsf(logdiffs[0]), fabsf(logdiffs[1]));
        if(maxlogdiff > DT_CACORRECTRGB_MAX_EV_DIFF)
        {
          const float correction_weight = DT_CACORRECTRGB_MAX_EV_DIFF / maxlogdiff;
          w *= correction_weight;
        }
        for(size_t kc = 0; kc <= 1; kc++)
        {
          const size_t c = (kc + guide + 1) % 3;
          manifold_lower[k * 4 + c] = logdiffs[kc] * w;
        }
        manifold_lower[k * 4 + guide] = fmaxf(in[k * 4 + guide], 0.0f) * w;
        manifold_lower[k * 4 + 3] = w;
        // manifold_higher still contains the values from first iteration
        // -> reset it.
        for(size_t c = 0; c < 4; c++)
        {
          manifold_higher[k * 4 + c] = 0.0f;
        }
      }
    }
 
    dt_gaussian_blur_4c(g, manifold_higher, blurred_manifold_higher);
    dt_gaussian_blur_4c(g, manifold_lower, blurred_manifold_lower);
    normalize_manifolds(blurred_in, blurred_manifold_lower, blurred_manifold_higher, width, height, guide);
    dt_gaussian_free(g);
  }
 
  // store all manifolds in the same structure to make upscaling faster
  __OMP_PARALLEL_FOR_SIMD__(aligned(manifolds, blurred_manifold_lower, blurred_manifold_higher:64))
  for(size_t k = 0; k < width * height; k++)
  {
    for(size_t c = 0; c < 3; c++)
    {
      manifolds[k * 6 + c] = blurred_manifold_higher[k * 4 + c];
      manifolds[k * 6 + 3 + c] = blurred_manifold_lower[k * 4 + c];
    }
  }
 
error:;
  dt_pixelpipe_cache_free_align(manifold_lower);
  dt_pixelpipe_cache_free_align(manifold_higher);
  dt_pixelpipe_cache_free_align(blurred_in);
  dt_pixelpipe_cache_free_align(blurred_manifold_lower);
  dt_pixelpipe_cache_free_align(blurred_manifold_higher);
  return err;
}
#undef DT_CACORRECTRGB_MAX_EV_DIFF
 
__DT_CLONE_TARGETS__
static void apply_correction(const float* const restrict in,
                          const float* const restrict manifolds,
                          const size_t width, const size_t height, const float sigma,
                          const dt_iop_cacorrectrgb_guide_channel_t guide,
                          const dt_iop_cacorrectrgb_mode_t mode,
                          float* const restrict out)
 
{
  __OMP_PARALLEL_FOR__()
  for(size_t k = 0; k < width * height; k++)
  {
    const float high_guide = fmaxf(manifolds[k * 6 + guide], 1E-6f);
    const float low_guide = fmaxf(manifolds[k * 6 + 3 + guide], 1E-6f);
    const float log_high = log2f(high_guide);
    const float log_low = log2f(low_guide);
    const float dist_low_high = log_high - log_low;
    const float pixelg = fmaxf(in[k * 4 + guide], 0.0f);
    const float log_pixg = log2f(fminf(fmaxf(pixelg, low_guide), high_guide));
 
    // determine how close our pixel is from the low manifold compared to the
    // high manifold.
    // if pixel value is lower or equal to the low manifold, weight_low = 1.0f
    // if pixel value is higher or equal to the high manifold, weight_low = 0.0f
    float weight_low = fabsf(log_high - log_pixg) / fmaxf(dist_low_high, 1E-6f);
    // if the manifolds are very close, we are likely to introduce discontinuities
    // and to have a meaningless "weight_low".
    // thus in these cases make dist closer to 0.5.
    // we set a threshold of 0.25f EV min.
    const float threshold_dist_low_high = 0.25f;
    if(dist_low_high < threshold_dist_low_high)
    {
      const float weight = dist_low_high / threshold_dist_low_high;
      // dist_low_high = threshold_dist_low_high => dist
      // dist_low_high = 0.0 => 0.5f
      weight_low = weight_low * weight + 0.5f * (1.0f - weight);
    }
    const float weight_high = fmaxf(1.0f - weight_low, 0.0f);
 
    for(size_t kc = 0; kc <= 1; kc++)
    {
      const size_t c = (guide + kc + 1) % 3;
      const float pixelc = fmaxf(in[k * 4 + c], 0.0f);
 
      const float ratio_high_manifolds = manifolds[k * 6 + c] / high_guide;
      const float ratio_low_manifolds = manifolds[k * 6 + 3 + c] / low_guide;
      // weighted geometric mean between the ratios.
      const float ratio = powf(ratio_low_manifolds, weight_low) * powf(ratio_high_manifolds, weight_high);
 
      const float outp = pixelg * ratio;
 
      switch(mode)
      {
        case DT_CACORRECT_MODE_STANDARD:
          out[k * 4 + c] = outp;
          break;
        case DT_CACORRECT_MODE_DARKEN:
          out[k * 4 + c] = fminf(outp, pixelc);
          break;
        case DT_CACORRECT_MODE_BRIGHTEN:
          out[k * 4 + c] = fmaxf(outp, pixelc);
          break;
      }
    }
 
    out[k * 4 + guide] = pixelg;
    out[k * 4 + 3] = in[k * 4 + 3];
  }
}
 
__DT_CLONE_TARGETS__
static int reduce_artifacts(const float* const restrict in,
                            const size_t width, const size_t height, const float sigma,
                            const dt_iop_cacorrectrgb_guide_channel_t guide,
                            const float safety,
                            float* const restrict out)
 
{
  int err = 0;
  dt_gaussian_t *g = NULL;
 
  // in_out contains the 2 guided channels of in, and the 2 guided channels of out
  // it allows to blur all channels in one 4-channel gaussian blur instead of 2
  float *const restrict DT_ALIGNED_PIXEL in_out = dt_pixelpipe_cache_alloc_align_float_cache(width * height * 4, 0);
  float *const restrict blurred_in_out = dt_pixelpipe_cache_alloc_align_float_cache(width * height * 4, 0);
  if(IS_NULL_PTR(blurred_in_out) || IS_NULL_PTR(in_out))
  {
    err = 1;
    goto error;
  }
  __OMP_PARALLEL_FOR__()
  for(size_t k = 0; k < width * height; k++)
  {
    for(size_t kc = 0; kc <= 1; kc++)
    {
      const size_t c = (guide + kc + 1) % 3;
      in_out[k * 4 + kc * 2 + 0] = in[k * 4 + c];
      in_out[k * 4 + kc * 2 + 1] = out[k * 4 + c];
    }
  }
 
 
  dt_aligned_pixel_t max = { INFINITY, INFINITY, INFINITY, INFINITY };
  dt_aligned_pixel_t min = {0.0f, 0.0f, 0.0f, 0.0f};
  g = dt_gaussian_init(width, height, 4, max, min, sigma, 0);
  if(IS_NULL_PTR(g))
  {
    err = 1;
    goto error;
  }
  dt_gaussian_blur_4c(g, in_out, blurred_in_out);
 
  // we consider that even with chromatic aberration, local average should
  // be close to be accurate.
  // thus, the local average of output should be similar to the one of the input
  // if they are not, the algorithm probably washed out colors too much or
  // may have produced artifacts.
  // we do a weighted average between input and output, keeping more input if
  // the local averages are very different.
  // we use the same weight for all channels, as using different weights
  // introduces artifacts in practice.
  __OMP_PARALLEL_FOR__()
  for(size_t k = 0; k < width * height; k++)
  {
    float w = 1.0f;
    for(size_t kc = 0; kc <= 1; kc++)
    {
      const float avg_in = log2f(fmaxf(blurred_in_out[k * 4 + kc * 2 + 0], 1E-6f));
      const float avg_out = log2f(fmaxf(blurred_in_out[k * 4 + kc * 2 + 1], 1E-6f));
      w *= expf(-fmaxf(fabsf(avg_out - avg_in), 0.01f) * safety);
    }
    for(size_t kc = 0; kc <= 1; kc++)
    {
      const size_t c = (guide + kc + 1) % 3;
      out[k * 4 + c] = fmaxf(1.0f - w, 0.0f) * fmaxf(in[k * 4 + c], 0.0f) + w * fmaxf(out[k * 4 + c], 0.0f);
    }
  }
 
error:;
  dt_pixelpipe_cache_free_align(blurred_in_out);
  if(g) dt_gaussian_free(g);
  dt_pixelpipe_cache_free_align(in_out);
  return err;
}
 
static inline __attribute__((always_inline)) int reduce_chromatic_aberrations(const float* const restrict in,
                          const size_t width, const size_t height,
                          const size_t ch, const float sigma, const float sigma2,
                          const dt_iop_cacorrectrgb_guide_channel_t guide,
                          const dt_iop_cacorrectrgb_mode_t mode,
                          const gboolean refine_manifolds,
                          const float safety,
                          float* const restrict out)
 
{
  int err = 0;
  const float downsize = fminf(3.0f, sigma);
  const size_t ds_width = width / downsize;
  const size_t ds_height = height / downsize;
  float *const restrict ds_in = dt_pixelpipe_cache_alloc_align_float_cache(ds_width * ds_height * 4, 0);
  float *const restrict manifolds = dt_pixelpipe_cache_alloc_align_float_cache(width * height * 6, 0);
 
  // we use only one variable for both higher and lower manifolds in order
  // to save time by doing only one bilinear interpolation instead of 2.
  float *const restrict ds_manifolds = dt_pixelpipe_cache_alloc_align_float_cache(ds_width * ds_height * 6, 0);
  if(IS_NULL_PTR(ds_manifolds) || IS_NULL_PTR(ds_in) || IS_NULL_PTR(manifolds))
  {
    err = 1;
    goto error;
  }
 
  // Downsample the image for speed-up
  interpolate_bilinear(in, width, height, ds_in, ds_width, ds_height, 4);
 
  // Compute manifolds
  if(get_manifolds(ds_in, ds_width, ds_height, sigma / downsize, sigma2 / downsize, guide, ds_manifolds, refine_manifolds))
  {
    err = 1;
    goto error;
  }
 
  // upscale manifolds
  interpolate_bilinear(ds_manifolds, ds_width, ds_height, manifolds, width, height, 6);
  apply_correction(in, manifolds, width, height, sigma, guide, mode, out);
  if(reduce_artifacts(in, width, height, sigma, guide, safety, out))
  {
    err = 1;
    goto error;
  }
 
error:;
  dt_pixelpipe_cache_free_align(ds_in);
  dt_pixelpipe_cache_free_align(manifolds);
  dt_pixelpipe_cache_free_align(ds_manifolds);
 
  return err;
}
 
int process(struct dt_iop_module_t *self, const dt_dev_pixelpipe_t *pipe, const dt_dev_pixelpipe_iop_t *piece, const void *const ivoid, void *const ovoid)
{
  const dt_iop_roi_t *const roi_in = &piece->roi_in;
  const dt_iop_roi_t *const roi_out = &piece->roi_out;
 
  dt_iop_cacorrectrgb_params_t *d = (dt_iop_cacorrectrgb_params_t *)piece->data;
  // used to adjuste blur level depending on size. Don't amplify noise if magnified > 100%
  const float scale = fmaxf(dt_dev_get_module_scale(pipe, roi_in), 1.f);
  const int ch = piece->dsc_in.channels;
  const size_t width = roi_out->width;
  const size_t height = roi_out->height;
  const float* in = (float*)ivoid;
  float* out = (float*)ovoid;
  const float sigma = fmaxf(d->radius / scale, 1.0f);
  const float sigma2 = fmaxf(d->radius * d->radius / scale, 1.0f);
 
  // whether to be very conservative in preserving the original image, or to
  // keep algorithm result even if it overshoots
  const float safety = powf(20.0f, 1.0f - d->strength);
  return reduce_chromatic_aberrations(in, width, height, ch, sigma, sigma2, d->guide_channel, d->mode, d->refine_manifolds, safety, out);
}
 
void gui_update(dt_iop_module_t *self)
{
  dt_iop_cacorrectrgb_gui_data_t *g = (dt_iop_cacorrectrgb_gui_data_t *)self->gui_data;
  dt_iop_cacorrectrgb_params_t *p = (dt_iop_cacorrectrgb_params_t *)self->params;
 
  gtk_toggle_button_set_active(GTK_TOGGLE_BUTTON(g->refine_manifolds), p->refine_manifolds);
}
 
void reload_defaults(dt_iop_module_t *module)
{
  dt_iop_cacorrectrgb_params_t *d = (dt_iop_cacorrectrgb_params_t *)module->default_params;
 
  d->guide_channel = DT_CACORRECT_RGB_G;
  d->radius = 5.0f;
  d->strength = 0.5f;
  d->mode = DT_CACORRECT_MODE_STANDARD;
  d->refine_manifolds = FALSE;
 
  dt_iop_cacorrectrgb_gui_data_t *g = (dt_iop_cacorrectrgb_gui_data_t *)module->gui_data;
  if(!IS_NULL_PTR(g))
  {
    dt_bauhaus_combobox_set_default(g->guide_channel, d->guide_channel);
    dt_bauhaus_slider_set_default(g->radius, d->radius);
    dt_bauhaus_slider_set_soft_range(g->radius, 1.0, 20.0);
    dt_bauhaus_slider_set_default(g->strength, d->strength);
    dt_bauhaus_combobox_set_default(g->mode, d->mode);
    gtk_toggle_button_set_active(GTK_TOGGLE_BUTTON(g->refine_manifolds), d->refine_manifolds);
  }
}
 
void gui_init(dt_iop_module_t *self)
{
  dt_iop_cacorrectrgb_gui_data_t *g = IOP_GUI_ALLOC(cacorrectrgb);
  self->widget = gtk_box_new(GTK_ORIENTATION_VERTICAL, DT_GUI_BOX_SPACING);
  g->guide_channel = dt_bauhaus_combobox_from_params(self, "guide_channel");
  gtk_widget_set_tooltip_text(g->guide_channel, _("channel used as a reference to\n"
                                           "correct the other channels.\n"
                                           "use sharpest channel if some\n"
                                           "channels are blurry.\n"
                                           "try changing guide channel if you\n"
                                           "have artifacts."));
  g->radius = dt_bauhaus_slider_from_params(self, "radius");
  gtk_widget_set_tooltip_text(g->radius, _("increase for stronger correction"));
  g->strength = dt_bauhaus_slider_from_params(self, "strength");
  gtk_widget_set_tooltip_text(g->strength, _("balance between smoothing colors\n"
                                             "and preserving them.\n"
                                             "high values can lead to overshooting\n"
                                             "and edge bleeding."));
 
  gtk_box_pack_start(GTK_BOX(self->widget), dt_ui_section_label_new(_("advanced parameters")), TRUE, TRUE, 0);
  g->mode = dt_bauhaus_combobox_from_params(self, "mode");
  gtk_widget_set_tooltip_text(g->mode, _("correction mode to use.\n"
                                         "can help with multiple\n"
                                         "instances for very damaged\n"
                                         "images.\n"
                                         "darken only is particularly\n"
                                         "efficient to correct blue\n"
                                         "chromatic aberration."));
  g->refine_manifolds = dt_bauhaus_toggle_from_params(self, "refine_manifolds");
  gtk_widget_set_tooltip_text(g->refine_manifolds, _("runs an iterative approach\n"
                                                    "with several radii.\n"
                                                    "improves result on images\n"
                                                    "with very large chromatic\n"
                                                    "aberrations, but can smooth\n"
                                                    "colors too much on other\n"
                                                    "images."));
}
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