Dr. Zongliang Gan
the College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications
Prof. Kaihua Zhang
Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology
Please feel free to email Zongliang Gan (ganzl@njupt.edu.cn)
We present an efficient and effective subspace-learning based image interpolation framework that can accurately estimate the high-resolution (HR) pixels by fusing a set of learning-based cubic interpolation results.
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This code is tested in Window 10(64), Matlab 2016a
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test_ip.m --- Evaluation on the simulated image datasets
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test_offline_opt.m -- Evaluation on offline C++
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test_realworld_ip --- Evaluation on the real-world image dataset
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The main image interpolation function codec ---- func\impccdf.m ---> hr = impccdf(lr, flag);
usage: input lr: input_image_data mxnxd flag==0 -->offline flag==1 ---> online
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The offline optimization version ---> ccdf.exe
usage: ccdf.exe input_image_file out_put_image_file offline
opencv_world310.dll is only used to read image file.
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set 5 set 14 imax : three classic image test sets
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urban 100 : image set used in
Jia-Bin Huang, Abhishek Singh, and [Narendra Ahuja] (http://vision.ai.illinois.edu/ahuja.html), "Single Image Super-Resolution from Transformed Self-Exemplars", CVPR 2015 PDF