Code for the ECCV 2018 Paper
Fast Light Field Reconstruction With Deep Coarse-To-Fine Modeling of Spatial-Angular Clues
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Please also read our TIP 2018 paper: "Light Field Spatial Super-resolution Using Deep Efficient Spatial-Angular Separable Convolution" with code below
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Description
A learning based model that generate a densely-sampled LF fast and accurately from a sparsely-sampled LF in one forward pass.
Requirements and Dependencies
- MATLAB
- cuda and cudnn (For GPU. Please modify install.m if not using cudnn)
- matconvnet (Please use the matconvnet code given in this repository. It contains the 4D convolution code written by us)
Installation
# Start MATLAB
$ matlab
>> install
Training
Set the training and validation data directory (opts.test_dir) in init_opts.m. Download the training and validation datasets to the specofoc directories. Make sure that there are enough memory for loading the whole training and validatoin datasets.
>> train
Testing Pretrained Models
Set the testing data directory (opts.test_dir) in init_opts.m
>> test
Testing Your Own Models
>> test_model(name, depth, gpu, saveImg, epoch, len)
- model_name : model name
- depth : model depth
- gpu : GPU ID
- saveImg : Save the HR SAIs if true
- epoch : model epoch to test
- len : controls the size of the sub-lightfield, value depends on GPU memory
Authors of the Paper
Henry W. F. Yeung, Junhui Hou, Jie Chen , Yuk Ying Chung and Xiaoming Chen
* Equal Contibutions