Implementation of CVPR2017 Paper: "Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution"(http://vllab1.ucmerced.edu/~wlai24/LapSRN/) in Chainer.
$ python train.py -h
usage: train.py [-h] [--dataset DATASET] [--outdirname OUTDIRNAME]
[--scale SCALE] [--batchsize BATCHSIZE] [--epoch EPOCH]
[--steps_per_epoch STEPS_PER_EPOCH] [--model MODEL] [--gpu GPU]
LapSRN
optional arguments:
-h, --help show this help message and exit
--dataset DATASET
--outdirname OUTDIRNAME
--scale SCALE
--batchsize BATCHSIZE
--epoch EPOCH
--steps_per_epoch STEPS_PER_EPOCH
--model MODEL
--gpu GPU
$ python sr.py -h
usage: sr.py [-h] [--model MODEL] [--image IMAGE] [--scale SCALE] [--gpu GPU]
LapSRN Super-Resolution
optional arguments:
-h, --help show this help message and exit
--model MODEL
--image IMAGE
--scale SCALE
--gpu GPU