Awesome
Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation
This is a tensorflow implementation of the paper. PDF
directory
./inputs/G/
Ground-truth video frames
./inputs/L/
Low-resolution video frames
./results/<L>L/G/
Outputs from given ground-truth video frames using <L> depth network
./results/<L>L/L/
Outputs from given low-resolution video frames using <L> depth network
test
Put your video frames to the input directory and run test.py
with arguments <R>
, <L>
and <T>
.
python test.py <R> <L> <T>
<R>
is the upscaling factor of 2, 3, 4.
<L>
is the depth of network of 16, 28, 52.
<T>
is the type of input frames, G
denotes GT inputs and L
denotes LR inputs.
For example, python test.py 4 16 G
super-resolve input frames in ./inputs/G/*
using 16
depth network with upscaling factor 4
.
(Possible combinations for <R> <L>
is 2 16
, 3 16
, 4 16
, 4 28
, and 4 52
.)
This code was tested under Python 2.7 and TensorFlow 1.3.0.
video
bibtex
@InProceedings{Jo_2018_CVPR,
author = {Jo, Younghyun and Oh, Seoung Wug and Kang, Jaeyeon and Kim, Seon Joo},
title = {Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2018}
}