PyTorch LapSRN

Implementation of CVPR2017 Paper: "Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution"( in PyTorch



usage: [-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)

An example of training usage is shown as follows:

python --cuda


usage: [-h] [--cuda] [--model MODEL] [--dataset DATASET]
               [--scale SCALE]

PyTorch LapSRN Eval

optional arguments:
  -h, --help         show this help message and exit
  --cuda             use cuda?
  --model MODEL      model path
  --dataset DATASET  dataset name, Default: Set5
  --scale SCALE      scale factor, Default: 4


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

PyTorch LapSRN Demo

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.


  • 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 31.54 31.65
Set14 28.19 28.27
BSD100 27.32 27.36


  • LapSRN x8
  • LapGAN Evaluation


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

    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}

Pytorch Lapsrn

Pytorch implementation for LapSRN (CVPR2017)

Pytorch Lapsrn Info

⭐ Stars 247
🔗 Source Code
🕒 Last Update 7 months ago
🕒 Created 5 years ago
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