PyTorch LapSRN

Implementation of CVPR2017 Paper: "Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution"(http://vllab1.ucmerced.edu/~wlai24/LapSRN/) in PyTorch

Usage

Training

usage: main.py [-h] [--batchSize BATCHSIZE] [--nEpochs NEPOCHS] [--lr LR]
               [--step STEP] [--cuda] [--resume RESUME]
               [--start-epoch START_EPOCH] [--threads THREADS]
               [--momentum MOMENTUM] [--weight-decay WEIGHT_DECAY]
               [--pretrained PRETRAINED]

PyTorch LapSRN

optional arguments:
  -h, --help            show this help message and exit
  --batchSize BATCHSIZE
                        training batch size
  --nEpochs NEPOCHS     number of epochs to train for
  --lr LR               Learning Rate. Default=1e-4
  --step STEP           Sets the learning rate to the initial LR decayed by
                        momentum every n epochs, Default: n=10
  --cuda                Use cuda?
  --resume RESUME       Path to checkpoint (default: none)
  --start-epoch START_EPOCH
                        Manual epoch number (useful on restarts)
  --threads THREADS     Number of threads for data loader to use, Default: 1
  --momentum MOMENTUM   Momentum, Default: 0.9
  --weight-decay WEIGHT_DECAY, --wd WEIGHT_DECAY
                        weight decay, Default: 1e-4
  --pretrained PRETRAINED
                        path to pretrained model (default: none)

Test

usage: test.py [-h] [--cuda] [--model MODEL] [--image IMAGE] [--scale SCALE]

PyTorch LapSRN Test

optional arguments:
  -h, --help     show this help message and exit
  --cuda         use cuda?
  --model MODEL  model path
  --image IMAGE  image name
  --scale SCALE  scale factor, Default: 4

We convert Set5 test set images to mat format using Matlab, for best PSNR performance, please use Matlab

Prepare Training dataset

We provide a simple hdf5 format training sample in data folder with 'data', 'label_x2', and 'label_x4' keys, the training data is generated with Matlab Bicubic Interplotation, please refer Code for Data Generation for creating training files.

Performance

We provide a pretrained LapSRN x4 model trained on T91 and BSDS200 images from SR_training_datasets with data augmentation as mentioned in the paper
No bias is used in this implementation, and another difference from paper is that Adam optimizer with 1e-4 learning is applied instead of SGD
Performance in PSNR on Set5, Set14, and BSD100

DataSet/Method	LapSRN Paper	LapSRN PyTorch
Set5	37.54	37.65
Set14	28.19	28.27
BSD100	27.32	27.36

ToDos

LapSRN x8
Code for data generation
LapGAN

Citation

If you find the code and datasets useful in your research, please cite:

@inproceedings{LapSRN,
    author    = {Lai, Wei-Sheng and Huang, Jia-Bin and Ahuja, Narendra and Yang, Ming-Hsuan}, 
    title     = {Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution}, 
    booktitle = {IEEE Conferene on Computer Vision and Pattern Recognition},
    year      = {2017}
}

smahajan07/pytorch-LapSRN