Matlab demo code for "Light Field Reconstruction Using Deep Convolutional Network on EPI" (CVPR 2017)
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
EPICNN
FSRCNN
SCN_Matlab-master
SRCNN
ScSR
VDSR-caffe-master
matconvnet
non-blind deconvolution
utils
.gitattributes
BlurKernel.mat
Early Access.pdf
README.md
install.m
main.m
main_batchProcessing.m

README.md


Matlab demo code for "Light Field Reconstruction Using Deep Convolutional Network on EPI" (CVPR 2017)


Note: The restoration kernels include SCN [1], SRSC [2], SRCNN [3], VDSR [4] and FSRCNN [5]. The non-blind deblur code is by Pan et al. [6].

Please cite our paper if you use this code, thank you!

@inproceedings{EPICNN17, author = {Gaochang Wu and Mandan Zhao and Liangyong Wang and Qionghai Dai and Tianyou Chai and Yebin Liu}, title = {Light Field Reconstruction Using Deep Convolutional Network on EPI}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017}, year = {2017}, }

[1] Zhaowen Wang, Ding Liu, Wei Han, Jianchao Yang and Thomas S. Huang, Deep Networks for Image Super-Resolution with Sparse Prior. International Conference on Computer Vision (ICCV), 2015

[2] J. Yang et al. Image super-resolution via sparse representation. IEEE Transactions on Image Processing, Vol 19, Issue 11, pp2861-2873, 2010

[3] Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. Image Super-Resolution Using Deep Convolutional Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015

[4] Jiwon Kim, Jung Kwon Lee and Kyoung Mu Lee, Accurate Image Super-Resolution Using Very Deep Convolutional Networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016

[5] Chao Dong, Chen Change Loy, Xiaoou Tang. Accelerating the Super-Resolution Convolutional Neural Network, in Proceedings of European Conference on Computer Vision (ECCV), 2016

[6] Jinshan Pan, Zhe Hu, Zhixun Su, and Ming-Hsuan Yang, Deblurring Text Images via L0-Regularized Intensity and Gradient Prior, CVPR 2014


Usage:

  1. Please download Lytro data at "http://lightfields.stanford.edu/", and save the data under the file named "Data".
  2. Before testing the code, please install "matconvnet" by running "install.m".
  3. Demo code is "main.m".
  4. Batch processing code is "main_batchProcessing.m".