Remove the rain in the input image/video by applying the derain methods based on convolutional neural networks. Supported models:
-
Recurrent Squeeze-and-Excitation Context Aggregation Net (RESCAN). See http://openaccess.thecvf.com/content_ECCV_2018/papers/Xia_Li_Recurrent_Squeeze-and-Excitation_Context_ECCV_2018_paper.pdf.
Training as well as model generation scripts are provided in the repository at https://github.com/XueweiMeng/derain_filter.git.
Native model files (.model) can be generated from TensorFlow model files (.pb) by using tools/python/convert.py
The filter accepts the following options:
- filter_type
-
Specify which filter to use. This option accepts the following values:
- derain
-
Derain filter. To conduct derain filter, you need to use a derain model.
- dehaze
-
Dehaze filter. To conduct dehaze filter, you need to use a dehaze model.
Default value is derain.
- dnn_backend
-
Specify which DNN backend to use for model loading and execution. This option accepts the following values:
- native
-
Native implementation of DNN loading and execution.
- tensorflow
-
TensorFlow backend. To enable this backend you need to install the TensorFlow for C library (see https://www.tensorflow.org/install/install_c) and configure FFmpeg with
--enable-libtensorflow
Default value is native.
- 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.
It can also be finished with dnn_processing filter.