libavfilter/vf_vaguedenoiser.c - manifest_repos/ffmpeg - Git at Google

 /*
  * Copyright (c) 2003 LeFunGus, lefungus@altern.org
  *
  * This file is part of FFmpeg
  *
  * FFmpeg 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 2 of the License, or
  * (at your option) any later version.
  *
  * FFmpeg 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 FFmpeg; if not, write to the Free Software Foundation, Inc.,
  * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
  */

 #include <float.h>

 #include "libavutil/imgutils.h"
 #include "libavutil/attributes.h"
 #include "libavutil/common.h"
 #include "libavutil/pixdesc.h"
 #include "libavutil/intreadwrite.h"
 #include "libavutil/opt.h"

 #include "avfilter.h"
 #include "formats.h"
 #include "internal.h"
 #include "video.h"

 typedef struct VagueDenoiserContext {
     const AVClass *class;

     float threshold;
     float percent;
     int method;
     int type;
     int nsteps;
     int planes;

     int depth;
     int bpc;
     int peak;
     int nb_planes;
     int planeheight[4];
     int planewidth[4];

     float *block;
     float *in;
     float *out;
     float *tmp;

     int hlowsize[4][32];
     int hhighsize[4][32];
     int vlowsize[4][32];
     int vhighsize[4][32];

     void (*thresholding)(float *block, const int width, const int height,
                          const int stride, const float threshold,
                          const float percent);
 } VagueDenoiserContext;

 #define OFFSET(x) offsetof(VagueDenoiserContext, x)
 #define FLAGS AV_OPT_FLAG_VIDEO_PARAM | AV_OPT_FLAG_FILTERING_PARAM
 static const AVOption vaguedenoiser_options[] = {
     { "threshold", "set filtering strength",   OFFSET(threshold), AV_OPT_TYPE_FLOAT, {.dbl=2.},  0,DBL_MAX, FLAGS },
     { "method",    "set filtering method",     OFFSET(method),    AV_OPT_TYPE_INT,   {.i64=2 },  0, 2,      FLAGS, "method" },
         { "hard",   "hard thresholding",       0,                 AV_OPT_TYPE_CONST, {.i64=0},   0, 0,      FLAGS, "method" },
         { "soft",   "soft thresholding",       0,                 AV_OPT_TYPE_CONST, {.i64=1},   0, 0,      FLAGS, "method" },
         { "garrote", "garrote thresholding",   0,                 AV_OPT_TYPE_CONST, {.i64=2},   0, 0,      FLAGS, "method" },
     { "nsteps",    "set number of steps",      OFFSET(nsteps),    AV_OPT_TYPE_INT,   {.i64=6 },  1, 32,     FLAGS },
     { "percent", "set percent of full denoising", OFFSET(percent),AV_OPT_TYPE_FLOAT, {.dbl=85},  0,100,     FLAGS },
     { "planes",    "set planes to filter",     OFFSET(planes),    AV_OPT_TYPE_INT,   {.i64=15 }, 0, 15,     FLAGS },
     { "type",    "set threshold type",     OFFSET(type),          AV_OPT_TYPE_INT,   {.i64=0 },  0, 1,      FLAGS, "type" },
         { "universal",  "universal (VisuShrink)", 0,              AV_OPT_TYPE_CONST, {.i64=0},   0, 0,      FLAGS, "type" },
         { "bayes",      "bayes (BayesShrink)",    0,              AV_OPT_TYPE_CONST, {.i64=1},   0, 0,      FLAGS, "type" },
     { NULL }
 };

 AVFILTER_DEFINE_CLASS(vaguedenoiser);

 #define NPAD 10

 static const float analysis_low[9] = {
     0.037828455506995f, -0.023849465019380f, -0.110624404418423f, 0.377402855612654f,
     0.852698679009403f, 0.377402855612654f, -0.110624404418423f, -0.023849465019380f, 0.037828455506995f
 };

 static const float analysis_high[7] = {
     -0.064538882628938f, 0.040689417609558f, 0.418092273222212f, -0.788485616405664f,
     0.418092273222212f, 0.040689417609558f, -0.064538882628938f
 };

 static const float synthesis_low[7] = {
     -0.064538882628938f, -0.040689417609558f, 0.418092273222212f, 0.788485616405664f,
     0.418092273222212f, -0.040689417609558f, -0.064538882628938f
 };

 static const float synthesis_high[9] = {
     -0.037828455506995f, -0.023849465019380f, 0.110624404418423f, 0.377402855612654f,
     -0.852698679009403f, 0.377402855612654f, 0.110624404418423f, -0.023849465019380f, -0.037828455506995f
 };

 static int query_formats(AVFilterContext *ctx)
 {
     static const enum AVPixelFormat pix_fmts[] = {
         AV_PIX_FMT_GRAY8, AV_PIX_FMT_GRAY9, AV_PIX_FMT_GRAY10,
         AV_PIX_FMT_GRAY12, AV_PIX_FMT_GRAY14, AV_PIX_FMT_GRAY16,
         AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P,
         AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P,
         AV_PIX_FMT_YUV440P, AV_PIX_FMT_YUV444P,
         AV_PIX_FMT_YUVJ420P, AV_PIX_FMT_YUVJ422P,
         AV_PIX_FMT_YUVJ440P, AV_PIX_FMT_YUVJ444P,
         AV_PIX_FMT_YUVJ411P,
         AV_PIX_FMT_YUV420P9, AV_PIX_FMT_YUV422P9, AV_PIX_FMT_YUV444P9,
         AV_PIX_FMT_YUV420P10, AV_PIX_FMT_YUV422P10, AV_PIX_FMT_YUV444P10,
         AV_PIX_FMT_YUV440P10,
         AV_PIX_FMT_YUV444P12, AV_PIX_FMT_YUV422P12, AV_PIX_FMT_YUV420P12,
         AV_PIX_FMT_YUV440P12,
         AV_PIX_FMT_YUV444P14, AV_PIX_FMT_YUV422P14, AV_PIX_FMT_YUV420P14,
         AV_PIX_FMT_YUV420P16, AV_PIX_FMT_YUV422P16, AV_PIX_FMT_YUV444P16,
         AV_PIX_FMT_GBRP, AV_PIX_FMT_GBRP9, AV_PIX_FMT_GBRP10,
         AV_PIX_FMT_GBRP12, AV_PIX_FMT_GBRP14, AV_PIX_FMT_GBRP16,
         AV_PIX_FMT_YUVA420P,  AV_PIX_FMT_YUVA422P,   AV_PIX_FMT_YUVA444P,
         AV_PIX_FMT_YUVA444P9, AV_PIX_FMT_YUVA444P10, AV_PIX_FMT_YUVA444P12, AV_PIX_FMT_YUVA444P16,
         AV_PIX_FMT_YUVA422P9, AV_PIX_FMT_YUVA422P10, AV_PIX_FMT_YUVA422P12, AV_PIX_FMT_YUVA422P16,
         AV_PIX_FMT_YUVA420P9, AV_PIX_FMT_YUVA420P10, AV_PIX_FMT_YUVA420P16,
         AV_PIX_FMT_GBRAP,     AV_PIX_FMT_GBRAP10,    AV_PIX_FMT_GBRAP12,    AV_PIX_FMT_GBRAP16,
         AV_PIX_FMT_NONE
     };
     AVFilterFormats *fmts_list = ff_make_format_list(pix_fmts);
     if (!fmts_list)
         return AVERROR(ENOMEM);
     return ff_set_common_formats(ctx, fmts_list);
 }

 static int config_input(AVFilterLink *inlink)
 {
     VagueDenoiserContext *s = inlink->dst->priv;
     const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(inlink->format);
     int p, i, nsteps_width, nsteps_height, nsteps_max;

     s->depth = desc->comp[0].depth;
     s->bpc = (s->depth + 7) / 8;
     s->nb_planes = desc->nb_components;

     s->planeheight[1] = s->planeheight[2] = AV_CEIL_RSHIFT(inlink->h, desc->log2_chroma_h);
     s->planeheight[0] = s->planeheight[3] = inlink->h;
     s->planewidth[1]  = s->planewidth[2]  = AV_CEIL_RSHIFT(inlink->w, desc->log2_chroma_w);
     s->planewidth[0]  = s->planewidth[3]  = inlink->w;

     s->block = av_malloc_array(inlink->w * inlink->h, sizeof(*s->block));
     s->in    = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->in));
     s->out   = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->out));
     s->tmp   = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->tmp));

     if (!s->block || !s->in || !s->out || !s->tmp)
         return AVERROR(ENOMEM);

     s->threshold *= 1 << (s->depth - 8);
     s->peak = (1 << s->depth) - 1;

     nsteps_width  = ((s->planes & 2 || s->planes & 4) && s->nb_planes > 1) ? s->planewidth[1] : s->planewidth[0];
     nsteps_height = ((s->planes & 2 || s->planes & 4) && s->nb_planes > 1) ? s->planeheight[1] : s->planeheight[0];

     for (nsteps_max = 1; nsteps_max < 15; nsteps_max++) {
         if (pow(2, nsteps_max) >= nsteps_width || pow(2, nsteps_max) >= nsteps_height)
             break;
     }

     s->nsteps = FFMIN(s->nsteps, nsteps_max - 2);

     for (p = 0; p < 4; p++) {
         s->hlowsize[p][0]  = (s->planewidth[p] + 1) >> 1;
         s->hhighsize[p][0] =  s->planewidth[p] >> 1;
         s->vlowsize[p][0]  = (s->planeheight[p] + 1) >> 1;
         s->vhighsize[p][0] =  s->planeheight[p] >> 1;

         for (i = 1; i < s->nsteps; i++) {
             s->hlowsize[p][i]  = (s->hlowsize[p][i - 1] + 1) >> 1;
             s->hhighsize[p][i] =  s->hlowsize[p][i - 1] >> 1;
             s->vlowsize[p][i]  = (s->vlowsize[p][i - 1] + 1) >> 1;
             s->vhighsize[p][i] =  s->vlowsize[p][i - 1] >> 1;
         }
     }

     return 0;
 }

 static inline void copy(const float *p1, float *p2, const int length)
 {
     memcpy(p2, p1, length * sizeof(float));
 }

 static inline void copyv(const float *p1, const int stride1, float *p2, const int length)
 {
     int i;

     for (i = 0; i < length; i++) {
         p2[i] = *p1;
         p1 += stride1;
     }
 }

 static inline void copyh(const float *p1, float *p2, const int stride2, const int length)
 {
     int i;

     for (i = 0; i < length; i++) {
         *p2 = p1[i];
         p2 += stride2;
     }
 }

 // Do symmetric extension of data using prescribed symmetries
 // Original values are in output[npad] through output[npad+size-1]
 // New values will be placed in output[0] through output[npad] and in output[npad+size] through output[2*npad+size-1] (note: end values may not be filled in)
 // extension at left bdry is ... 3 2 1 0 | 0 1 2 3 ...
 // same for right boundary
 // if right_ext=1 then ... 3 2 1 0 | 1 2 3
 static void symmetric_extension(float *output, const int size, const int left_ext, const int right_ext)
 {
     int first = NPAD;
     int last = NPAD - 1 + size;
     const int originalLast = last;
     int i, nextend, idx;

     if (left_ext == 2)
         output[--first] = output[NPAD];
     if (right_ext == 2)
         output[++last] = output[originalLast];

     // extend left end
     nextend = first;
     for (i = 0; i < nextend; i++)
         output[--first] = output[NPAD + 1 + i];

     idx = NPAD + NPAD - 1 + size;

     // extend right end
     nextend = idx - last;
     for (i = 0; i < nextend; i++)
         output[++last] = output[originalLast - 1 - i];
 }

 static void transform_step(float *input, float *output, const int size, const int low_size, VagueDenoiserContext *s)
 {
     int i;

     symmetric_extension(input, size, 1, 1);

     for (i = NPAD; i < NPAD + low_size; i++) {
         const float a = input[2 * i - 14] * analysis_low[0];
         const float b = input[2 * i - 13] * analysis_low[1];
         const float c = input[2 * i - 12] * analysis_low[2];
         const float d = input[2 * i - 11] * analysis_low[3];
         const float e = input[2 * i - 10] * analysis_low[4];
         const float f = input[2 * i -  9] * analysis_low[3];
         const float g = input[2 * i -  8] * analysis_low[2];
         const float h = input[2 * i -  7] * analysis_low[1];
         const float k = input[2 * i -  6] * analysis_low[0];

         output[i] = a + b + c + d + e + f + g + h + k;
     }

     for (i = NPAD; i < NPAD + low_size; i++) {
         const float a = input[2 * i - 12] * analysis_high[0];
         const float b = input[2 * i - 11] * analysis_high[1];
         const float c = input[2 * i - 10] * analysis_high[2];
         const float d = input[2 * i -  9] * analysis_high[3];
         const float e = input[2 * i -  8] * analysis_high[2];
         const float f = input[2 * i -  7] * analysis_high[1];
         const float g = input[2 * i -  6] * analysis_high[0];

         output[i + low_size] = a + b + c + d + e + f + g;
     }
 }

 static void invert_step(const float *input, float *output, float *temp, const int size, VagueDenoiserContext *s)
 {
     const int low_size = (size + 1) >> 1;
     const int high_size = size >> 1;
     int left_ext = 1, right_ext, i;
     int findex;

     memcpy(temp + NPAD, input + NPAD, low_size * sizeof(float));

     right_ext = (size % 2 == 0) ? 2 : 1;
     symmetric_extension(temp, low_size, left_ext, right_ext);

     memset(output, 0, (NPAD + NPAD + size) * sizeof(float));
     findex = (size + 2) >> 1;

     for (i = 9; i < findex + 11; i++) {
         const float a = temp[i] * synthesis_low[0];
         const float b = temp[i] * synthesis_low[1];
         const float c = temp[i] * synthesis_low[2];
         const float d = temp[i] * synthesis_low[3];

         output[2 * i - 13] += a;
         output[2 * i - 12] += b;
         output[2 * i - 11] += c;
         output[2 * i - 10] += d;
         output[2 * i -  9] += c;
         output[2 * i -  8] += b;
         output[2 * i -  7] += a;
     }

     memcpy(temp + NPAD, input + NPAD + low_size, high_size * sizeof(float));

     left_ext = 2;
     right_ext = (size % 2 == 0) ? 1 : 2;
     symmetric_extension(temp, high_size, left_ext, right_ext);

     for (i = 8; i < findex + 11; i++) {
         const float a = temp[i] * synthesis_high[0];
         const float b = temp[i] * synthesis_high[1];
         const float c = temp[i] * synthesis_high[2];
         const float d = temp[i] * synthesis_high[3];
         const float e = temp[i] * synthesis_high[4];

         output[2 * i - 13] += a;
         output[2 * i - 12] += b;
         output[2 * i - 11] += c;
         output[2 * i - 10] += d;
         output[2 * i -  9] += e;
         output[2 * i -  8] += d;
         output[2 * i -  7] += c;
         output[2 * i -  6] += b;
         output[2 * i -  5] += a;
     }
 }

 static void hard_thresholding(float *block, const int width, const int height,
                               const int stride, const float threshold,
                               const float percent)
 {
     const float frac = 1.f - percent * 0.01f;
     int y, x;

     for (y = 0; y < height; y++) {
         for (x = 0; x < width; x++) {
             if (FFABS(block[x]) <= threshold)
                 block[x] *= frac;
         }
         block += stride;
     }
 }

 static void soft_thresholding(float *block, const int width, const int height, const int stride,
                               const float threshold, const float percent)
 {
     const float frac = 1.f - percent * 0.01f;
     const float shift = threshold * 0.01f * percent;
     int y, x;

     for (y = 0; y < height; y++) {
         for (x = 0; x < width; x++) {
             const float temp = FFABS(block[x]);
             if (temp <= threshold)
                 block[x] *= frac;
             else
                 block[x] = (block[x] < 0.f ? -1.f : (block[x] > 0.f ? 1.f : 0.f)) * (temp - shift);
         }
         block += stride;
     }
 }

 static void qian_thresholding(float *block, const int width, const int height,
                               const int stride, const float threshold,
                               const float percent)
 {
     const float percent01 = percent * 0.01f;
     const float tr2 = threshold * threshold * percent01;
     const float frac = 1.f - percent01;
     int y, x;

     for (y = 0; y < height; y++) {
         for (x = 0; x < width; x++) {
             const float temp = FFABS(block[x]);
             if (temp <= threshold) {
                 block[x] *= frac;
             } else {
                 const float tp2 = temp * temp;
                 block[x] *= (tp2 - tr2) / tp2;
             }
         }
         block += stride;
     }
 }

 static float bayes_threshold(float *block, const int width, const int height,
                               const int stride, const float threshold)
 {
     float mean = 0.f;

     for (int y = 0; y < height; y++) {
         for (int x = 0; x < width; x++) {
             mean += block[x] * block[x];
         }
         block += stride;
     }

     mean /= width * height;

     return threshold * threshold / (FFMAX(sqrtf(mean - threshold), FLT_EPSILON));
 }

 static void filter(VagueDenoiserContext *s, AVFrame *in, AVFrame *out)
 {
     int p, y, x, i, j;

     for (p = 0; p < s->nb_planes; p++) {
         const int height = s->planeheight[p];
         const int width = s->planewidth[p];
         const uint8_t *srcp8 = in->data[p];
         const uint16_t *srcp16 = (const uint16_t *)in->data[p];
         uint8_t *dstp8 = out->data[p];
         uint16_t *dstp16 = (uint16_t *)out->data[p];
         float *output = s->block;
         int h_low_size0 = width;
         int v_low_size0 = height;
         int nsteps_transform = s->nsteps;
         int nsteps_invert = s->nsteps;
         const float *input = s->block;

         if (!((1 << p) & s->planes)) {
             av_image_copy_plane(out->data[p], out->linesize[p], in->data[p], in->linesize[p],
                                 s->planewidth[p] * s->bpc, s->planeheight[p]);
             continue;
         }

         if (s->depth <= 8) {
             for (y = 0; y < height; y++) {
                 for (x = 0; x < width; x++)
                     output[x] = srcp8[x];
                 srcp8 += in->linesize[p];
                 output += width;
             }
         } else {
             for (y = 0; y < height; y++) {
                 for (x = 0; x < width; x++)
                     output[x] = srcp16[x];
                 srcp16 += in->linesize[p] / 2;
                 output += width;
             }
         }

         while (nsteps_transform--) {
             int low_size = (h_low_size0 + 1) >> 1;
             float *input = s->block;
             for (j = 0; j < v_low_size0; j++) {
                 copy(input, s->in + NPAD, h_low_size0);
                 transform_step(s->in, s->out, h_low_size0, low_size, s);
                 copy(s->out + NPAD, input, h_low_size0);
                 input += width;
             }

             low_size = (v_low_size0 + 1) >> 1;
             input = s->block;
             for (j = 0; j < h_low_size0; j++) {
                 copyv(input, width, s->in + NPAD, v_low_size0);
                 transform_step(s->in, s->out, v_low_size0, low_size, s);
                 copyh(s->out + NPAD, input, width, v_low_size0);
                 input++;
             }

             h_low_size0 = (h_low_size0 + 1) >> 1;
             v_low_size0 = (v_low_size0 + 1) >> 1;
         }

         if (s->type == 0) {
             s->thresholding(s->block, width, height, width, s->threshold, s->percent);
         } else {
             for (int n = 0; n < s->nsteps; n++) {
                 float threshold;
                 float *block;

                 if (n == s->nsteps - 1) {
                     threshold = bayes_threshold(s->block, s->hlowsize[p][n], s->vlowsize[p][n], width, s->threshold);
                     s->thresholding(s->block, s->hlowsize[p][n], s->vlowsize[p][n], width, threshold, s->percent);
                 }
                 block = s->block + s->hlowsize[p][n];
                 threshold = bayes_threshold(block, s->hhighsize[p][n], s->vlowsize[p][n], width, s->threshold);
                 s->thresholding(block, s->hhighsize[p][n], s->vlowsize[p][n], width, threshold, s->percent);
                 block = s->block + s->vlowsize[p][n] * width;
                 threshold = bayes_threshold(block, s->hlowsize[p][n], s->vhighsize[p][n], width, s->threshold);
                 s->thresholding(block, s->hlowsize[p][n], s->vhighsize[p][n], width, threshold, s->percent);
                 block = s->block + s->hlowsize[p][n] + s->vlowsize[p][n] * width;
                 threshold = bayes_threshold(block, s->hhighsize[p][n], s->vhighsize[p][n], width, s->threshold);
                 s->thresholding(block, s->hhighsize[p][n], s->vhighsize[p][n], width, threshold, s->percent);
             }
         }

         while (nsteps_invert--) {
             const int idx = s->vlowsize[p][nsteps_invert]  + s->vhighsize[p][nsteps_invert];
             const int idx2 = s->hlowsize[p][nsteps_invert] + s->hhighsize[p][nsteps_invert];
             float * idx3 = s->block;
             for (i = 0; i < idx2; i++) {
                 copyv(idx3, width, s->in + NPAD, idx);
                 invert_step(s->in, s->out, s->tmp, idx, s);
                 copyh(s->out + NPAD, idx3, width, idx);
                 idx3++;
             }

             idx3 = s->block;
             for (i = 0; i < idx; i++) {
                 copy(idx3, s->in + NPAD, idx2);
                 invert_step(s->in, s->out, s->tmp, idx2, s);
                 copy(s->out + NPAD, idx3, idx2);
                 idx3 += width;
             }
         }

         if (s->depth <= 8) {
             for (y = 0; y < height; y++) {
                 for (x = 0; x < width; x++)
                     dstp8[x] = av_clip_uint8(input[x] + 0.5f);
                 input += width;
                 dstp8 += out->linesize[p];
             }
         } else {
             for (y = 0; y < height; y++) {
                 for (x = 0; x < width; x++)
                     dstp16[x] = av_clip(input[x] + 0.5f, 0, s->peak);
                 input += width;
                 dstp16 += out->linesize[p] / 2;
             }
         }
     }
 }

 static int filter_frame(AVFilterLink *inlink, AVFrame *in)
 {
     AVFilterContext *ctx  = inlink->dst;
     VagueDenoiserContext *s = ctx->priv;
     AVFilterLink *outlink = ctx->outputs[0];
     AVFrame *out;
     int direct = av_frame_is_writable(in);

     if (direct) {
         out = in;
     } else {
         out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
         if (!out) {
             av_frame_free(&in);
             return AVERROR(ENOMEM);
         }

         av_frame_copy_props(out, in);
     }

     filter(s, in, out);

     if (!direct)
         av_frame_free(&in);

     return ff_filter_frame(outlink, out);
 }

 static av_cold int init(AVFilterContext *ctx)
 {
     VagueDenoiserContext *s = ctx->priv;

     switch (s->method) {
     case 0:
         s->thresholding = hard_thresholding;
         break;
     case 1:
         s->thresholding = soft_thresholding;
         break;
     case 2:
         s->thresholding = qian_thresholding;
         break;
     }

     return 0;
 }

 static av_cold void uninit(AVFilterContext *ctx)
 {
     VagueDenoiserContext *s = ctx->priv;

     av_freep(&s->block);
     av_freep(&s->in);
     av_freep(&s->out);
     av_freep(&s->tmp);
 }

 static const AVFilterPad vaguedenoiser_inputs[] = {
     {
         .name         = "default",
         .type         = AVMEDIA_TYPE_VIDEO,
         .config_props = config_input,
         .filter_frame = filter_frame,
     },
     { NULL }
 };


 static const AVFilterPad vaguedenoiser_outputs[] = {
     {
         .name = "default",
         .type = AVMEDIA_TYPE_VIDEO
     },
     { NULL }
 };

 AVFilter ff_vf_vaguedenoiser = {
     .name          = "vaguedenoiser",
     .description   = NULL_IF_CONFIG_SMALL("Apply a Wavelet based Denoiser."),
     .priv_size     = sizeof(VagueDenoiserContext),
     .priv_class    = &vaguedenoiser_class,
     .init          = init,
     .uninit        = uninit,
     .query_formats = query_formats,
     .inputs        = vaguedenoiser_inputs,
     .outputs       = vaguedenoiser_outputs,
     .flags         = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC,
 };
	/*
	* Copyright (c) 2003 LeFunGus, lefungus@altern.org
	*
	* This file is part of FFmpeg
	*
	* FFmpeg 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 2 of the License, or
	* (at your option) any later version.
	*
	* FFmpeg 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 FFmpeg; if not, write to the Free Software Foundation, Inc.,
	* 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
	*/

	#include <float.h>

	#include "libavutil/imgutils.h"
	#include "libavutil/attributes.h"
	#include "libavutil/common.h"
	#include "libavutil/pixdesc.h"
	#include "libavutil/intreadwrite.h"
	#include "libavutil/opt.h"

	#include "avfilter.h"
	#include "formats.h"
	#include "internal.h"
	#include "video.h"

	typedef struct VagueDenoiserContext {
	const AVClass *class;

	float threshold;
	float percent;
	int method;
	int type;
	int nsteps;
	int planes;

	int depth;
	int bpc;
	int peak;
	int nb_planes;
	int planeheight[4];
	int planewidth[4];

	float *block;
	float *in;
	float *out;
	float *tmp;

	int hlowsize[4][32];
	int hhighsize[4][32];
	int vlowsize[4][32];
	int vhighsize[4][32];

	void (thresholding)(float block, const int width, const int height,
	const int stride, const float threshold,
	const float percent);
	} VagueDenoiserContext;

	#define OFFSET(x) offsetof(VagueDenoiserContext, x)
	#define FLAGS AV_OPT_FLAG_VIDEO_PARAM \| AV_OPT_FLAG_FILTERING_PARAM
	static const AVOption vaguedenoiser_options[] = {
	{ "threshold", "set filtering strength", OFFSET(threshold), AV_OPT_TYPE_FLOAT, {.dbl=2.}, 0,DBL_MAX, FLAGS },
	{ "method", "set filtering method", OFFSET(method), AV_OPT_TYPE_INT, {.i64=2 }, 0, 2, FLAGS, "method" },
	{ "hard", "hard thresholding", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "method" },
	{ "soft", "soft thresholding", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "method" },
	{ "garrote", "garrote thresholding", 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "method" },
	{ "nsteps", "set number of steps", OFFSET(nsteps), AV_OPT_TYPE_INT, {.i64=6 }, 1, 32, FLAGS },
	{ "percent", "set percent of full denoising", OFFSET(percent),AV_OPT_TYPE_FLOAT, {.dbl=85}, 0,100, FLAGS },
	{ "planes", "set planes to filter", OFFSET(planes), AV_OPT_TYPE_INT, {.i64=15 }, 0, 15, FLAGS },
	{ "type", "set threshold type", OFFSET(type), AV_OPT_TYPE_INT, {.i64=0 }, 0, 1, FLAGS, "type" },
	{ "universal", "universal (VisuShrink)", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "type" },
	{ "bayes", "bayes (BayesShrink)", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "type" },
	{ NULL }
	};

	AVFILTER_DEFINE_CLASS(vaguedenoiser);

	#define NPAD 10

	static const float analysis_low[9] = {
	0.037828455506995f, -0.023849465019380f, -0.110624404418423f, 0.377402855612654f,
	0.852698679009403f, 0.377402855612654f, -0.110624404418423f, -0.023849465019380f, 0.037828455506995f
	};

	static const float analysis_high[7] = {
	-0.064538882628938f, 0.040689417609558f, 0.418092273222212f, -0.788485616405664f,
	0.418092273222212f, 0.040689417609558f, -0.064538882628938f
	};

	static const float synthesis_low[7] = {
	-0.064538882628938f, -0.040689417609558f, 0.418092273222212f, 0.788485616405664f,
	0.418092273222212f, -0.040689417609558f, -0.064538882628938f
	};

	static const float synthesis_high[9] = {
	-0.037828455506995f, -0.023849465019380f, 0.110624404418423f, 0.377402855612654f,
	-0.852698679009403f, 0.377402855612654f, 0.110624404418423f, -0.023849465019380f, -0.037828455506995f
	};

	static int query_formats(AVFilterContext *ctx)
	{
	static const enum AVPixelFormat pix_fmts[] = {
	AV_PIX_FMT_GRAY8, AV_PIX_FMT_GRAY9, AV_PIX_FMT_GRAY10,
	AV_PIX_FMT_GRAY12, AV_PIX_FMT_GRAY14, AV_PIX_FMT_GRAY16,
	AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P,
	AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P,
	AV_PIX_FMT_YUV440P, AV_PIX_FMT_YUV444P,
	AV_PIX_FMT_YUVJ420P, AV_PIX_FMT_YUVJ422P,
	AV_PIX_FMT_YUVJ440P, AV_PIX_FMT_YUVJ444P,
	AV_PIX_FMT_YUVJ411P,
	AV_PIX_FMT_YUV420P9, AV_PIX_FMT_YUV422P9, AV_PIX_FMT_YUV444P9,
	AV_PIX_FMT_YUV420P10, AV_PIX_FMT_YUV422P10, AV_PIX_FMT_YUV444P10,
	AV_PIX_FMT_YUV440P10,
	AV_PIX_FMT_YUV444P12, AV_PIX_FMT_YUV422P12, AV_PIX_FMT_YUV420P12,
	AV_PIX_FMT_YUV440P12,
	AV_PIX_FMT_YUV444P14, AV_PIX_FMT_YUV422P14, AV_PIX_FMT_YUV420P14,
	AV_PIX_FMT_YUV420P16, AV_PIX_FMT_YUV422P16, AV_PIX_FMT_YUV444P16,
	AV_PIX_FMT_GBRP, AV_PIX_FMT_GBRP9, AV_PIX_FMT_GBRP10,
	AV_PIX_FMT_GBRP12, AV_PIX_FMT_GBRP14, AV_PIX_FMT_GBRP16,
	AV_PIX_FMT_YUVA420P, AV_PIX_FMT_YUVA422P, AV_PIX_FMT_YUVA444P,
	AV_PIX_FMT_YUVA444P9, AV_PIX_FMT_YUVA444P10, AV_PIX_FMT_YUVA444P12, AV_PIX_FMT_YUVA444P16,
	AV_PIX_FMT_YUVA422P9, AV_PIX_FMT_YUVA422P10, AV_PIX_FMT_YUVA422P12, AV_PIX_FMT_YUVA422P16,
	AV_PIX_FMT_YUVA420P9, AV_PIX_FMT_YUVA420P10, AV_PIX_FMT_YUVA420P16,
	AV_PIX_FMT_GBRAP, AV_PIX_FMT_GBRAP10, AV_PIX_FMT_GBRAP12, AV_PIX_FMT_GBRAP16,
	AV_PIX_FMT_NONE
	};
	AVFilterFormats *fmts_list = ff_make_format_list(pix_fmts);
	if (!fmts_list)
	return AVERROR(ENOMEM);
	return ff_set_common_formats(ctx, fmts_list);
	}

	static int config_input(AVFilterLink *inlink)
	{
	VagueDenoiserContext *s = inlink->dst->priv;
	const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(inlink->format);
	int p, i, nsteps_width, nsteps_height, nsteps_max;

	s->depth = desc->comp[0].depth;
	s->bpc = (s->depth + 7) / 8;
	s->nb_planes = desc->nb_components;

	s->planeheight[1] = s->planeheight[2] = AV_CEIL_RSHIFT(inlink->h, desc->log2_chroma_h);
	s->planeheight[0] = s->planeheight[3] = inlink->h;
	s->planewidth[1] = s->planewidth[2] = AV_CEIL_RSHIFT(inlink->w, desc->log2_chroma_w);
	s->planewidth[0] = s->planewidth[3] = inlink->w;

	s->block = av_malloc_array(inlink->w * inlink->h, sizeof(*s->block));
	s->in = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->in));
	s->out = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->out));
	s->tmp = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->tmp));

	if (!s->block \|\| !s->in \|\| !s->out \|\| !s->tmp)
	return AVERROR(ENOMEM);

	s->threshold *= 1 << (s->depth - 8);
	s->peak = (1 << s->depth) - 1;

	nsteps_width = ((s->planes & 2 \|\| s->planes & 4) && s->nb_planes > 1) ? s->planewidth[1] : s->planewidth[0];
	nsteps_height = ((s->planes & 2 \|\| s->planes & 4) && s->nb_planes > 1) ? s->planeheight[1] : s->planeheight[0];

	for (nsteps_max = 1; nsteps_max < 15; nsteps_max++) {
	if (pow(2, nsteps_max) >= nsteps_width \|\| pow(2, nsteps_max) >= nsteps_height)
	break;
	}

	s->nsteps = FFMIN(s->nsteps, nsteps_max - 2);

	for (p = 0; p < 4; p++) {
	s->hlowsize[p][0] = (s->planewidth[p] + 1) >> 1;
	s->hhighsize[p][0] = s->planewidth[p] >> 1;
	s->vlowsize[p][0] = (s->planeheight[p] + 1) >> 1;
	s->vhighsize[p][0] = s->planeheight[p] >> 1;

	for (i = 1; i < s->nsteps; i++) {
	s->hlowsize[p][i] = (s->hlowsize[p][i - 1] + 1) >> 1;
	s->hhighsize[p][i] = s->hlowsize[p][i - 1] >> 1;
	s->vlowsize[p][i] = (s->vlowsize[p][i - 1] + 1) >> 1;
	s->vhighsize[p][i] = s->vlowsize[p][i - 1] >> 1;
	}
	}

	return 0;
	}

	static inline void copy(const float p1, float p2, const int length)
	{
	memcpy(p2, p1, length * sizeof(float));
	}

	static inline void copyv(const float p1, const int stride1, float p2, const int length)
	{
	int i;

	for (i = 0; i < length; i++) {
	p2[i] = *p1;
	p1 += stride1;
	}
	}

	static inline void copyh(const float p1, float p2, const int stride2, const int length)
	{
	int i;

	for (i = 0; i < length; i++) {
	*p2 = p1[i];
	p2 += stride2;
	}
	}

	// Do symmetric extension of data using prescribed symmetries
	// Original values are in output[npad] through output[npad+size-1]
	// New values will be placed in output[0] through output[npad] and in output[npad+size] through output[2*npad+size-1] (note: end values may not be filled in)
	// extension at left bdry is ... 3 2 1 0 \| 0 1 2 3 ...
	// same for right boundary
	// if right_ext=1 then ... 3 2 1 0 \| 1 2 3
	static void symmetric_extension(float *output, const int size, const int left_ext, const int right_ext)
	{
	int first = NPAD;
	int last = NPAD - 1 + size;
	const int originalLast = last;
	int i, nextend, idx;

	if (left_ext == 2)
	output[--first] = output[NPAD];
	if (right_ext == 2)
	output[++last] = output[originalLast];

	// extend left end
	nextend = first;
	for (i = 0; i < nextend; i++)
	output[--first] = output[NPAD + 1 + i];

	idx = NPAD + NPAD - 1 + size;

	// extend right end
	nextend = idx - last;
	for (i = 0; i < nextend; i++)
	output[++last] = output[originalLast - 1 - i];
	}

	static void transform_step(float input, float output, const int size, const int low_size, VagueDenoiserContext *s)
	{
	int i;

	symmetric_extension(input, size, 1, 1);

	for (i = NPAD; i < NPAD + low_size; i++) {
	const float a = input[2 * i - 14] * analysis_low[0];
	const float b = input[2 * i - 13] * analysis_low[1];
	const float c = input[2 * i - 12] * analysis_low[2];
	const float d = input[2 * i - 11] * analysis_low[3];
	const float e = input[2 * i - 10] * analysis_low[4];
	const float f = input[2 * i - 9] * analysis_low[3];
	const float g = input[2 * i - 8] * analysis_low[2];
	const float h = input[2 * i - 7] * analysis_low[1];
	const float k = input[2 * i - 6] * analysis_low[0];

	output[i] = a + b + c + d + e + f + g + h + k;
	}

	for (i = NPAD; i < NPAD + low_size; i++) {
	const float a = input[2 * i - 12] * analysis_high[0];
	const float b = input[2 * i - 11] * analysis_high[1];
	const float c = input[2 * i - 10] * analysis_high[2];
	const float d = input[2 * i - 9] * analysis_high[3];
	const float e = input[2 * i - 8] * analysis_high[2];
	const float f = input[2 * i - 7] * analysis_high[1];
	const float g = input[2 * i - 6] * analysis_high[0];

	output[i + low_size] = a + b + c + d + e + f + g;
	}
	}

	static void invert_step(const float input, float output, float temp, const int size, VagueDenoiserContext s)
	{
	const int low_size = (size + 1) >> 1;
	const int high_size = size >> 1;
	int left_ext = 1, right_ext, i;
	int findex;

	memcpy(temp + NPAD, input + NPAD, low_size * sizeof(float));

	right_ext = (size % 2 == 0) ? 2 : 1;
	symmetric_extension(temp, low_size, left_ext, right_ext);

	memset(output, 0, (NPAD + NPAD + size) * sizeof(float));
	findex = (size + 2) >> 1;

	for (i = 9; i < findex + 11; i++) {
	const float a = temp[i] * synthesis_low[0];
	const float b = temp[i] * synthesis_low[1];
	const float c = temp[i] * synthesis_low[2];
	const float d = temp[i] * synthesis_low[3];

	output[2 * i - 13] += a;
	output[2 * i - 12] += b;
	output[2 * i - 11] += c;
	output[2 * i - 10] += d;
	output[2 * i - 9] += c;
	output[2 * i - 8] += b;
	output[2 * i - 7] += a;
	}

	memcpy(temp + NPAD, input + NPAD + low_size, high_size * sizeof(float));

	left_ext = 2;
	right_ext = (size % 2 == 0) ? 1 : 2;
	symmetric_extension(temp, high_size, left_ext, right_ext);

	for (i = 8; i < findex + 11; i++) {
	const float a = temp[i] * synthesis_high[0];
	const float b = temp[i] * synthesis_high[1];
	const float c = temp[i] * synthesis_high[2];
	const float d = temp[i] * synthesis_high[3];
	const float e = temp[i] * synthesis_high[4];

	output[2 * i - 13] += a;
	output[2 * i - 12] += b;
	output[2 * i - 11] += c;
	output[2 * i - 10] += d;
	output[2 * i - 9] += e;
	output[2 * i - 8] += d;
	output[2 * i - 7] += c;
	output[2 * i - 6] += b;
	output[2 * i - 5] += a;
	}
	}

	static void hard_thresholding(float *block, const int width, const int height,
	const int stride, const float threshold,
	const float percent)
	{
	const float frac = 1.f - percent * 0.01f;
	int y, x;

	for (y = 0; y < height; y++) {
	for (x = 0; x < width; x++) {
	if (FFABS(block[x]) <= threshold)
	block[x] *= frac;
	}
	block += stride;
	}
	}

	static void soft_thresholding(float *block, const int width, const int height, const int stride,
	const float threshold, const float percent)
	{
	const float frac = 1.f - percent * 0.01f;
	const float shift = threshold * 0.01f * percent;
	int y, x;

	for (y = 0; y < height; y++) {
	for (x = 0; x < width; x++) {
	const float temp = FFABS(block[x]);
	if (temp <= threshold)
	block[x] *= frac;
	else
	block[x] = (block[x] < 0.f ? -1.f : (block[x] > 0.f ? 1.f : 0.f)) * (temp - shift);
	}
	block += stride;
	}
	}

	static void qian_thresholding(float *block, const int width, const int height,
	const int stride, const float threshold,
	const float percent)
	{
	const float percent01 = percent * 0.01f;
	const float tr2 = threshold * threshold * percent01;
	const float frac = 1.f - percent01;
	int y, x;

	for (y = 0; y < height; y++) {
	for (x = 0; x < width; x++) {
	const float temp = FFABS(block[x]);
	if (temp <= threshold) {
	block[x] *= frac;
	} else {
	const float tp2 = temp * temp;
	block[x] *= (tp2 - tr2) / tp2;
	}
	}
	block += stride;
	}
	}

	static float bayes_threshold(float *block, const int width, const int height,
	const int stride, const float threshold)
	{
	float mean = 0.f;

	for (int y = 0; y < height; y++) {
	for (int x = 0; x < width; x++) {
	mean += block[x] * block[x];
	}
	block += stride;
	}

	mean /= width * height;

	return threshold * threshold / (FFMAX(sqrtf(mean - threshold), FLT_EPSILON));
	}

	static void filter(VagueDenoiserContext s, AVFrame in, AVFrame *out)
	{
	int p, y, x, i, j;

	for (p = 0; p < s->nb_planes; p++) {
	const int height = s->planeheight[p];
	const int width = s->planewidth[p];
	const uint8_t *srcp8 = in->data[p];
	const uint16_t srcp16 = (const uint16_t )in->data[p];
	uint8_t *dstp8 = out->data[p];
	uint16_t dstp16 = (uint16_t )out->data[p];
	float *output = s->block;
	int h_low_size0 = width;
	int v_low_size0 = height;
	int nsteps_transform = s->nsteps;
	int nsteps_invert = s->nsteps;
	const float *input = s->block;

	if (!((1 << p) & s->planes)) {
	av_image_copy_plane(out->data[p], out->linesize[p], in->data[p], in->linesize[p],
	s->planewidth[p] * s->bpc, s->planeheight[p]);
	continue;
	}

	if (s->depth <= 8) {
	for (y = 0; y < height; y++) {
	for (x = 0; x < width; x++)
	output[x] = srcp8[x];
	srcp8 += in->linesize[p];
	output += width;
	}
	} else {
	for (y = 0; y < height; y++) {
	for (x = 0; x < width; x++)
	output[x] = srcp16[x];
	srcp16 += in->linesize[p] / 2;
	output += width;
	}
	}

	while (nsteps_transform--) {
	int low_size = (h_low_size0 + 1) >> 1;
	float *input = s->block;
	for (j = 0; j < v_low_size0; j++) {
	copy(input, s->in + NPAD, h_low_size0);
	transform_step(s->in, s->out, h_low_size0, low_size, s);
	copy(s->out + NPAD, input, h_low_size0);
	input += width;
	}

	low_size = (v_low_size0 + 1) >> 1;
	input = s->block;
	for (j = 0; j < h_low_size0; j++) {
	copyv(input, width, s->in + NPAD, v_low_size0);
	transform_step(s->in, s->out, v_low_size0, low_size, s);
	copyh(s->out + NPAD, input, width, v_low_size0);
	input++;
	}

	h_low_size0 = (h_low_size0 + 1) >> 1;
	v_low_size0 = (v_low_size0 + 1) >> 1;
	}

	if (s->type == 0) {
	s->thresholding(s->block, width, height, width, s->threshold, s->percent);
	} else {
	for (int n = 0; n < s->nsteps; n++) {
	float threshold;
	float *block;

	if (n == s->nsteps - 1) {
	threshold = bayes_threshold(s->block, s->hlowsize[p][n], s->vlowsize[p][n], width, s->threshold);
	s->thresholding(s->block, s->hlowsize[p][n], s->vlowsize[p][n], width, threshold, s->percent);
	}
	block = s->block + s->hlowsize[p][n];
	threshold = bayes_threshold(block, s->hhighsize[p][n], s->vlowsize[p][n], width, s->threshold);
	s->thresholding(block, s->hhighsize[p][n], s->vlowsize[p][n], width, threshold, s->percent);
	block = s->block + s->vlowsize[p][n] * width;
	threshold = bayes_threshold(block, s->hlowsize[p][n], s->vhighsize[p][n], width, s->threshold);
	s->thresholding(block, s->hlowsize[p][n], s->vhighsize[p][n], width, threshold, s->percent);
	block = s->block + s->hlowsize[p][n] + s->vlowsize[p][n] * width;
	threshold = bayes_threshold(block, s->hhighsize[p][n], s->vhighsize[p][n], width, s->threshold);
	s->thresholding(block, s->hhighsize[p][n], s->vhighsize[p][n], width, threshold, s->percent);
	}
	}

	while (nsteps_invert--) {
	const int idx = s->vlowsize[p][nsteps_invert] + s->vhighsize[p][nsteps_invert];
	const int idx2 = s->hlowsize[p][nsteps_invert] + s->hhighsize[p][nsteps_invert];
	float * idx3 = s->block;
	for (i = 0; i < idx2; i++) {
	copyv(idx3, width, s->in + NPAD, idx);
	invert_step(s->in, s->out, s->tmp, idx, s);
	copyh(s->out + NPAD, idx3, width, idx);
	idx3++;
	}

	idx3 = s->block;
	for (i = 0; i < idx; i++) {
	copy(idx3, s->in + NPAD, idx2);
	invert_step(s->in, s->out, s->tmp, idx2, s);
	copy(s->out + NPAD, idx3, idx2);
	idx3 += width;
	}
	}

	if (s->depth <= 8) {
	for (y = 0; y < height; y++) {
	for (x = 0; x < width; x++)
	dstp8[x] = av_clip_uint8(input[x] + 0.5f);
	input += width;
	dstp8 += out->linesize[p];
	}
	} else {
	for (y = 0; y < height; y++) {
	for (x = 0; x < width; x++)
	dstp16[x] = av_clip(input[x] + 0.5f, 0, s->peak);
	input += width;
	dstp16 += out->linesize[p] / 2;
	}
	}
	}
	}

	static int filter_frame(AVFilterLink inlink, AVFrame in)
	{
	AVFilterContext *ctx = inlink->dst;
	VagueDenoiserContext *s = ctx->priv;
	AVFilterLink *outlink = ctx->outputs[0];
	AVFrame *out;
	int direct = av_frame_is_writable(in);

	if (direct) {
	out = in;
	} else {
	out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
	if (!out) {
	av_frame_free(&in);
	return AVERROR(ENOMEM);
	}

	av_frame_copy_props(out, in);
	}

	filter(s, in, out);

	if (!direct)
	av_frame_free(&in);

	return ff_filter_frame(outlink, out);
	}

	static av_cold int init(AVFilterContext *ctx)
	{
	VagueDenoiserContext *s = ctx->priv;

	switch (s->method) {
	case 0:
	s->thresholding = hard_thresholding;
	break;
	case 1:
	s->thresholding = soft_thresholding;
	break;
	case 2:
	s->thresholding = qian_thresholding;
	break;
	}

	return 0;
	}

	static av_cold void uninit(AVFilterContext *ctx)
	{
	VagueDenoiserContext *s = ctx->priv;

	av_freep(&s->block);
	av_freep(&s->in);
	av_freep(&s->out);
	av_freep(&s->tmp);
	}

	static const AVFilterPad vaguedenoiser_inputs[] = {
	{
	.name = "default",
	.type = AVMEDIA_TYPE_VIDEO,
	.config_props = config_input,
	.filter_frame = filter_frame,
	},
	{ NULL }
	};


	static const AVFilterPad vaguedenoiser_outputs[] = {
	{
	.name = "default",
	.type = AVMEDIA_TYPE_VIDEO
	},
	{ NULL }
	};

	AVFilter ff_vf_vaguedenoiser = {
	.name = "vaguedenoiser",
	.description = NULL_IF_CONFIG_SMALL("Apply a Wavelet based Denoiser."),
	.priv_size = sizeof(VagueDenoiserContext),
	.priv_class = &vaguedenoiser_class,
	.init = init,
	.uninit = uninit,
	.query_formats = query_formats,
	.inputs = vaguedenoiser_inputs,
	.outputs = vaguedenoiser_outputs,
	.flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC,
	};