diff --git a/configure b/configure
index 875b77f..4b3964d 100755
--- a/configure
+++ b/configure
@@ -3463,6 +3463,7 @@ derain_filter_select="dnn"
 deshake_filter_select="pixelutils"
 deshake_opencl_filter_deps="opencl"
 dilation_opencl_filter_deps="opencl"
+dnn_processing_filter_select="dnn"
 drawtext_filter_deps="libfreetype"
 drawtext_filter_suggest="libfontconfig libfribidi"
 elbg_filter_deps="avcodec"
diff --git a/doc/filters.texi b/doc/filters.texi
index 9d387be..15771ab 100644
--- a/doc/filters.texi
+++ b/doc/filters.texi
@@ -8928,6 +8928,50 @@ ffmpeg -i INPUT -f lavfi -i nullsrc=hd720,geq='r=128+80*(sin(sqrt((X-W/2)*(X-W/2
 @end example
 @end itemize
 
+@section dnn_processing
+
+Do image processing with deep neural networks. Currently only AVFrame with RGB24
+and BGR24 are supported, more formats will be added later.
+
+The filter accepts the following options:
+
+@table @option
+@item dnn_backend
+Specify which DNN backend to use for model loading and execution. This option accepts
+the following values:
+
+@table @samp
+@item native
+Native implementation of DNN loading and execution.
+
+@item tensorflow
+TensorFlow backend. To enable this backend you
+need to install the TensorFlow for C library (see
+@url{https://www.tensorflow.org/install/install_c}) and configure FFmpeg with
+@code{--enable-libtensorflow}
+@end table
+
+Default value is @samp{native}.
+
+@item model
+Set path to model file specifying network architecture and its parameters.
+Note that different backends use different file formats. TensorFlow and native
+backend can load files for only its format.
+
+Native model file (.model) can be generated from TensorFlow model file (.pb) by using tools/python/convert.py
+
+@item input
+Set the input name of the dnn network.
+
+@item output
+Set the output name of the dnn network.
+
+@item fmt
+Set the pixel format for the Frame. Allowed values are @code{AV_PIX_FMT_RGB24}, and @code{AV_PIX_FMT_BGR24}.
+Default value is @code{AV_PIX_FMT_RGB24}.
+
+@end table
+
 @section drawbox
 
 Draw a colored box on the input image.
diff --git a/libavfilter/Makefile b/libavfilter/Makefile
index 2080eed..3eff398 100644
--- a/libavfilter/Makefile
+++ b/libavfilter/Makefile
@@ -223,6 +223,7 @@ OBJS-$(CONFIG_DILATION_FILTER)               += vf_neighbor.o
 OBJS-$(CONFIG_DILATION_OPENCL_FILTER)        += vf_neighbor_opencl.o opencl.o \
                                                 opencl/neighbor.o
 OBJS-$(CONFIG_DISPLACE_FILTER)               += vf_displace.o framesync.o
+OBJS-$(CONFIG_DNN_PROCESSING_FILTER)         += vf_dnn_processing.o
 OBJS-$(CONFIG_DOUBLEWEAVE_FILTER)            += vf_weave.o
 OBJS-$(CONFIG_DRAWBOX_FILTER)                += vf_drawbox.o
 OBJS-$(CONFIG_DRAWGRAPH_FILTER)              += f_drawgraph.o
diff --git a/libavfilter/allfilters.c b/libavfilter/allfilters.c
index cb60906..7c1e19e 100644
--- a/libavfilter/allfilters.c
+++ b/libavfilter/allfilters.c
@@ -209,6 +209,7 @@ extern AVFilter ff_vf_detelecine;
 extern AVFilter ff_vf_dilation;
 extern AVFilter ff_vf_dilation_opencl;
 extern AVFilter ff_vf_displace;
+extern AVFilter ff_vf_dnn_processing;
 extern AVFilter ff_vf_doubleweave;
 extern AVFilter ff_vf_drawbox;
 extern AVFilter ff_vf_drawgraph;
diff --git a/libavfilter/vf_dnn_processing.c b/libavfilter/vf_dnn_processing.c
new file mode 100644
index 0000000..87ad354
--- /dev/null
+++ b/libavfilter/vf_dnn_processing.c
@@ -0,0 +1,331 @@
+/*
+ * Copyright (c) 2019 Guo Yejun
+ *
+ * This file is part of FFmpeg.
+ *
+ * FFmpeg is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Lesser General Public
+ * License as published by the Free Software Foundation; either
+ * version 2.1 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
+ * Lesser General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser 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
+ */
+
+/**
+ * @file
+ * implementing a generic image processing filter using deep learning networks.
+ */
+
+#include "libavformat/avio.h"
+#include "libavutil/opt.h"
+#include "libavutil/pixdesc.h"
+#include "libavutil/avassert.h"
+#include "avfilter.h"
+#include "dnn_interface.h"
+#include "formats.h"
+#include "internal.h"
+
+typedef struct DnnProcessingContext {
+    const AVClass *class;
+
+    char *model_filename;
+    DNNBackendType backend_type;
+    enum AVPixelFormat fmt;
+    char *model_inputname;
+    char *model_outputname;
+
+    DNNModule *dnn_module;
+    DNNModel *model;
+
+    // input & output of the model at execution time
+    DNNData input;
+    DNNData output;
+} DnnProcessingContext;
+
+#define OFFSET(x) offsetof(DnnProcessingContext, x)
+#define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM
+static const AVOption dnn_processing_options[] = {
+    { "dnn_backend", "DNN backend",                OFFSET(backend_type),     AV_OPT_TYPE_INT,       { .i64 = 0 },    0, 1, FLAGS, "backend" },
+    { "native",      "native backend flag",        0,                        AV_OPT_TYPE_CONST,     { .i64 = 0 },    0, 0, FLAGS, "backend" },
+#if (CONFIG_LIBTENSORFLOW == 1)
+    { "tensorflow",  "tensorflow backend flag",    0,                        AV_OPT_TYPE_CONST,     { .i64 = 1 },    0, 0, FLAGS, "backend" },
+#endif
+    { "model",       "path to model file",         OFFSET(model_filename),   AV_OPT_TYPE_STRING,    { .str = NULL }, 0, 0, FLAGS },
+    { "input",       "input name of the model",    OFFSET(model_inputname),  AV_OPT_TYPE_STRING,    { .str = NULL }, 0, 0, FLAGS },
+    { "output",      "output name of the model",   OFFSET(model_outputname), AV_OPT_TYPE_STRING,    { .str = NULL }, 0, 0, FLAGS },
+    { "fmt",         "AVPixelFormat of the frame", OFFSET(fmt),              AV_OPT_TYPE_PIXEL_FMT, { .i64=AV_PIX_FMT_RGB24 }, AV_PIX_FMT_NONE, AV_PIX_FMT_NB - 1, FLAGS },
+    { NULL }
+};
+
+AVFILTER_DEFINE_CLASS(dnn_processing);
+
+static av_cold int init(AVFilterContext *context)
+{
+    DnnProcessingContext *ctx = context->priv;
+    int supported = 0;
+    // as the first step, only rgb24 and bgr24 are supported
+    const enum AVPixelFormat supported_pixel_fmts[] = {
+        AV_PIX_FMT_RGB24,
+        AV_PIX_FMT_BGR24,
+    };
+    for (int i = 0; i < sizeof(supported_pixel_fmts) / sizeof(enum AVPixelFormat); ++i) {
+        if (supported_pixel_fmts[i] == ctx->fmt) {
+            supported = 1;
+            break;
+        }
+    }
+    if (!supported) {
+        av_log(context, AV_LOG_ERROR, "pixel fmt %s not supported yet\n",
+                                       av_get_pix_fmt_name(ctx->fmt));
+        return AVERROR(AVERROR_INVALIDDATA);
+    }
+
+    if (!ctx->model_filename) {
+        av_log(ctx, AV_LOG_ERROR, "model file for network is not specified\n");
+        return AVERROR(EINVAL);
+    }
+    if (!ctx->model_inputname) {
+        av_log(ctx, AV_LOG_ERROR, "input name of the model network is not specified\n");
+        return AVERROR(EINVAL);
+    }
+    if (!ctx->model_outputname) {
+        av_log(ctx, AV_LOG_ERROR, "output name of the model network is not specified\n");
+        return AVERROR(EINVAL);
+    }
+
+    ctx->dnn_module = ff_get_dnn_module(ctx->backend_type);
+    if (!ctx->dnn_module) {
+        av_log(ctx, AV_LOG_ERROR, "could not create DNN module for requested backend\n");
+        return AVERROR(ENOMEM);
+    }
+    if (!ctx->dnn_module->load_model) {
+        av_log(ctx, AV_LOG_ERROR, "load_model for network is not specified\n");
+        return AVERROR(EINVAL);
+    }
+
+    ctx->model = (ctx->dnn_module->load_model)(ctx->model_filename);
+    if (!ctx->model) {
+        av_log(ctx, AV_LOG_ERROR, "could not load DNN model\n");
+        return AVERROR(EINVAL);
+    }
+
+    return 0;
+}
+
+static int query_formats(AVFilterContext *context)
+{
+    AVFilterFormats *formats;
+    DnnProcessingContext *ctx = context->priv;
+    enum AVPixelFormat pixel_fmts[2];
+    pixel_fmts[0] = ctx->fmt;
+    pixel_fmts[1] = AV_PIX_FMT_NONE;
+
+    formats = ff_make_format_list(pixel_fmts);
+    return ff_set_common_formats(context, formats);
+}
+
+static int config_input(AVFilterLink *inlink)
+{
+    AVFilterContext *context     = inlink->dst;
+    DnnProcessingContext *ctx = context->priv;
+    DNNReturnType result;
+    DNNData dnn_data;
+
+    result = ctx->model->get_input(ctx->model->model, &dnn_data, ctx->model_inputname);
+    if (result != DNN_SUCCESS) {
+        av_log(ctx, AV_LOG_ERROR, "could not get input from the model\n");
+        return AVERROR(EIO);
+    }
+
+    // the design is to add explicit scale filter before this filter
+    if (dnn_data.height != -1 && dnn_data.height != inlink->h) {
+        av_log(ctx, AV_LOG_ERROR, "the model requires frame height %d but got %d\n",
+                                   dnn_data.height, inlink->h);
+        return AVERROR(EIO);
+    }
+    if (dnn_data.width != -1 && dnn_data.width != inlink->w) {
+        av_log(ctx, AV_LOG_ERROR, "the model requires frame width %d but got %d\n",
+                                   dnn_data.width, inlink->w);
+        return AVERROR(EIO);
+    }
+
+    if (dnn_data.channels != 3) {
+        av_log(ctx, AV_LOG_ERROR, "the model requires input channels %d\n",
+                                   dnn_data.channels);
+        return AVERROR(EIO);
+    }
+    if (dnn_data.dt != DNN_FLOAT && dnn_data.dt != DNN_UINT8) {
+        av_log(ctx, AV_LOG_ERROR, "only support dnn models with input data type as float32 and uint8.\n");
+        return AVERROR(EIO);
+    }
+
+    ctx->input.width    = inlink->w;
+    ctx->input.height   = inlink->h;
+    ctx->input.channels = dnn_data.channels;
+    ctx->input.dt = dnn_data.dt;
+
+    result = (ctx->model->set_input_output)(ctx->model->model,
+                                        &ctx->input, ctx->model_inputname,
+                                        (const char **)&ctx->model_outputname, 1);
+    if (result != DNN_SUCCESS) {
+        av_log(ctx, AV_LOG_ERROR, "could not set input and output for the model\n");
+        return AVERROR(EIO);
+    }
+
+    return 0;
+}
+
+static int config_output(AVFilterLink *outlink)
+{
+    AVFilterContext *context = outlink->src;
+    DnnProcessingContext *ctx = context->priv;
+    DNNReturnType result;
+
+    // have a try run in case that the dnn model resize the frame
+    result = (ctx->dnn_module->execute_model)(ctx->model, &ctx->output, 1);
+    if (result != DNN_SUCCESS){
+        av_log(ctx, AV_LOG_ERROR, "failed to execute model\n");
+        return AVERROR(EIO);
+    }
+
+    outlink->w = ctx->output.width;
+    outlink->h = ctx->output.height;
+
+    return 0;
+}
+
+static int copy_from_frame_to_dnn(DNNData *dnn_data, const AVFrame *in)
+{
+    // extend this function to support more formats
+    av_assert0(in->format == AV_PIX_FMT_RGB24 || in->format == AV_PIX_FMT_RGB24);
+
+    if (dnn_data->dt == DNN_FLOAT) {
+        float *dnn_input = dnn_data->data;
+        for (int i = 0; i < in->height; i++) {
+            for(int j = 0; j < in->width * 3; j++) {
+                int k = i * in->linesize[0] + j;
+                int t = i * in->width * 3 + j;
+                dnn_input[t] = in->data[0][k] / 255.0f;
+            }
+        }
+    } else {
+        uint8_t *dnn_input = dnn_data->data;
+        av_assert0(dnn_data->dt == DNN_UINT8);
+        for (int i = 0; i < in->height; i++) {
+            for(int j = 0; j < in->width * 3; j++) {
+                int k = i * in->linesize[0] + j;
+                int t = i * in->width * 3 + j;
+                dnn_input[t] = in->data[0][k];
+            }
+        }
+    }
+
+    return 0;
+}
+
+static int copy_from_dnn_to_frame(AVFrame *out, const DNNData *dnn_data)
+{
+    // extend this function to support more formats
+    av_assert0(out->format == AV_PIX_FMT_RGB24 || out->format == AV_PIX_FMT_RGB24);
+
+    if (dnn_data->dt == DNN_FLOAT) {
+        float *dnn_output = dnn_data->data;
+        for (int i = 0; i < out->height; i++) {
+            for(int j = 0; j < out->width * 3; j++) {
+                int k = i * out->linesize[0] + j;
+                int t = i * out->width * 3 + j;
+                out->data[0][k] = av_clip((int)(dnn_output[t] * 255.0f), 0, 255);
+            }
+        }
+    } else {
+        uint8_t *dnn_output = dnn_data->data;
+        av_assert0(dnn_data->dt == DNN_UINT8);
+        for (int i = 0; i < out->height; i++) {
+            for(int j = 0; j < out->width * 3; j++) {
+                int k = i * out->linesize[0] + j;
+                int t = i * out->width * 3 + j;
+                out->data[0][k] = dnn_output[t];
+            }
+        }
+    }
+
+    return 0;
+}
+
+static int filter_frame(AVFilterLink *inlink, AVFrame *in)
+{
+    AVFilterContext *context  = inlink->dst;
+    AVFilterLink *outlink = context->outputs[0];
+    DnnProcessingContext *ctx = context->priv;
+    DNNReturnType dnn_result;
+    AVFrame *out;
+
+    copy_from_frame_to_dnn(&ctx->input, in);
+
+    dnn_result = (ctx->dnn_module->execute_model)(ctx->model, &ctx->output, 1);
+    if (dnn_result != DNN_SUCCESS){
+        av_log(ctx, AV_LOG_ERROR, "failed to execute model\n");
+        av_frame_free(&in);
+        return AVERROR(EIO);
+    }
+    av_assert0(ctx->output.channels == 3);
+
+    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);
+    copy_from_dnn_to_frame(out, &ctx->output);
+    av_frame_free(&in);
+    return ff_filter_frame(outlink, out);
+}
+
+static av_cold void uninit(AVFilterContext *ctx)
+{
+    DnnProcessingContext *context = ctx->priv;
+
+    if (context->dnn_module)
+        (context->dnn_module->free_model)(&context->model);
+
+    av_freep(&context->dnn_module);
+}
+
+static const AVFilterPad dnn_processing_inputs[] = {
+    {
+        .name         = "default",
+        .type         = AVMEDIA_TYPE_VIDEO,
+        .config_props = config_input,
+        .filter_frame = filter_frame,
+    },
+    { NULL }
+};
+
+static const AVFilterPad dnn_processing_outputs[] = {
+    {
+        .name = "default",
+        .type = AVMEDIA_TYPE_VIDEO,
+        .config_props  = config_output,
+    },
+    { NULL }
+};
+
+AVFilter ff_vf_dnn_processing = {
+    .name          = "dnn_processing",
+    .description   = NULL_IF_CONFIG_SMALL("Apply DNN processing filter to the input."),
+    .priv_size     = sizeof(DnnProcessingContext),
+    .init          = init,
+    .uninit        = uninit,
+    .query_formats = query_formats,
+    .inputs        = dnn_processing_inputs,
+    .outputs       = dnn_processing_outputs,
+    .priv_class    = &dnn_processing_class,
+};
