LQSR
Learning-based joint super-resolution and deblocking for a highly compressed image
Install / Use
npx skills add jesse1029/LQSRInstalls into whichever agent you are using.
README
Program: Single Image Super-Resolution of a Highly Compressed Image Authors: Chih-Chung Hsu (m121754@gmail.com) Citation format: L.W. Kang, C.C. Hsu, B.Q. Zhuang, C.W. Lin, and C.H. Yeh, “Learning-based joint super-resolution and deblocking for a highly compressed image,” IEEE Transactions on Multimedia, vol. 17, no. 7, pp. 921−934, July 2015.
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Note that our parameters may different from paper described. However, our performance of this released code is similar to that of paper.
The main file is "Demo_Proposed.m". In commend line in Matlab, you can type Demo_Proposed to get the final super-resolved results. Note that this code only can run in Matlab 32bit version.
If there is any problem, please contact with me.
===================================================================== Environments
Programming language: Matlab 2014b 32bit OS: Windows 10 preview x64 Database: Collected from ScSR code.
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