CNN을 활용한 Super Resolution

Complete documentation is available at 2018 졸업과제 최종 보고서 - 선명하조.pdf

2018 발표자료(9차) - 선명하조.pdf

2018 포스터 - 선명하조.png

2018 시연영상 - 선명하조 (Youtube).mp4

Performance

Setup and Installation of CNN을 활용한 Super Resolution

Getting CNN을 활용한 Super Resolution installed and ready-to-go should only take a few minutes.

demo.teamclear.cf

demo site is a quick and easy way to sharpen and improve video quality on the web.

Local Installation

Requirements

Installing CNN을 활용한 Super Resolution is easy and straightforward. Your system just needs to meet these two requirements:

• Python v3.6 (v3.4.0 and above is recommended)
• FFmpeg 3.4+, FFprobe
  • https://evermeet.cx/ffmpeg/ (windows)
• Windows, Linux, Unix, or Mac OS X

1. Clone Repository

git clone https://github.com/taking/TeamClear.git

2. install requirement

cd python-web

pip3 install -r requirements.txt

3. Execute

python3 TeamClear.py

Install with Python

The best way to install CNNTeamclear is via Python Package Index (PyPI). At the terminal prompt, simply run the following command to install CNNTeamclear:

$ pip install CNNTeamclear (Not Yet)

Reference

Thesis - "Accurate Image Super-Resolution Using Very Deep Convolutional Networks", CVPR 16'.

Data - "Train Data 1", "Test Data2"