FABNet-PyTorch

This is a pytorch project for the paper FABNet: Frequency-Aware Binarized Network for Single Image Super-Resolution by Xinrui Jiang, Nannan Wang, Jingwei Xin, Keyu Li, Xi Yang, Jie Li, Xiaoyu Wang and Xinbo Gao

Introduction

This paper presents a frequency-aware binarized network (FABNet) for single image super-resolution. We decompose the image features into low-frequency and high frequency components and then adopt a "divide-and-conquer" strategy to process them with well-designed binary network structures.

Dependencies and Installation

conda create -n FABNet python=3.7
conda activate FABNet
conda install pytorch=1.2 torchvision=0.4 cudatoolkit=10.0 -c pytorch
pip install pytorch-wavelets matplotlib scikit-image opencv-python h5py tqdm

Dataset

You can use the following links to download the datasets:
DIV2K dataset https://cv.snu.ac.kr/research/EDSR/DIV2K.tar
Set5 http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html
Set14 https://sites.google.com/site/romanzeyde/research-interests
BSD100 https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/
Urban100 https://sites.google.com/site/jbhuang0604/publications/struct_sr

Pretrained Model

You can download pre-trained models from: https://pan.baidu.com/s/13SDor2gkPMCmWlp7PZ592A (code：i49h) You can download results from: https://pan.baidu.com/s/1xYEUcNV2_ZY486NhCRSNkg (code：5jpy)

Usage

Train
Train the model on the corresponding network using the train config. For example, the training on FABNetC12B4:
train_model: import model.FABNet_tiny as model
python train.py --n_feats 12
The training on FABNetC96B8:
train_model: import model.FABNet_large as model
python train.py --n_feats 96
Test
Test the model on the corresponding network using the test config with the pretrained model. For example, the testing on FABNetC12B4:
train_model: import model.FABNet_tiny as model
python test.py --data_type img --n_feats 12 --pre_model */FABNet_B4C12_X2.pth