LFNet_TEST

LFNet: A Novel Bidirectional Recurrent Convolutional Neural Network for Light-field Image Super-resolution

Yunlong Wang, Fei Liu, Kunbo Zhang, Guangqi Hou, Zhenan Sun, Tieniu Tan

@ARTICLE{LFNet_Wang_2018,

author={Y. Wang and F. Liu and K. Zhang and G. Hou and Z. Sun and T. Tan},

journal={IEEE Transactions on Image Processing},

title={LFNet: A Novel Bidirectional Recurrent Convolutional Neural Network for Light-Field Image Super-Resolution},

year={2018}, volume={27}, number={9}, pages={4274-4286},

doi={10.1109/TIP.2018.2834819}, ISSN={1057-7149}, month={Sept}}

webpage

Datasets

Your dataset for quantitative evaluations would be better to contain LF scenes which are stored in .mat files. Ground truth 4D LF data L(u,v,s,t) will be loaded into gt_data variable, while its couterpart LR data will be loaded into lr_data.

Dependencies

• theano
• scikit-image
• numpy
• h5py
• argparse

Of course, you can install these packages with pip install command

sudo pip install argparse h5py numpy scikit-image theano

Usage

Run the following command line in terminal to evaluate the pre-trained LFNet model on LF Scenes stored under the folder DATA_FOLDER with .mat files named SCENE_NAME1.mat, SCENE_NAME2.mat, SCENE_NAME3.mat

THEANO_FLAGS=mode=FAST_RUN,device=cuda0,floatX=float32 python LFNet_Test_Mat_With_log.py --path ./DATA_FOLDER --scene SCENE_NAME1 SCENE_NAME2 SCENE_NAME3 --model_path ./model -F 4 -T 7 -C 7 -S

• THEANO_FLAGS=mode=FAST_RUN,device=cuda0,floatX=float32 specifies configurations of Theano packages
• --path will load the datasets for evalution from this path
• --scene stands for the namelist of LF scenes
• --model_path will load the pre-trained models for evaluation from this path
• -F stands for upsampling factor (default 4x as in the paper, 2x 3x models also supported)
• -T specifies angular resolution of training LF data (only support choices from [7,9])
• -C specifies angular resolution of LF data for evaluation
• -S save results

The results will be saved under the folder named DATA_FOLDER_eval_l7_f4 in this script. Meanwhile, a .log file named LFNet_Test.log and a .mat file named performance_stat.mat will be generated as output, recording details of the evaluation process (date, model options, PSNR, SSIM, Elapsed Time and so on)