DocumentCode :
3740347
Title :
Super-resolution: Sparse dictionary design method using quantitative comparison
Author :
Marwa Moustafa;Hala M. Ebeid;Ashraf Helmy;Taymoor M. Nazamy;Mohamed F. Tolba
Author_Institution :
Data Reception, Analysis and Receiving Station Affairs, National Authority for Remote Sensing and Space Science, Cairo, Egypt
fYear :
2015
Firstpage :
383
Lastpage :
389
Abstract :
Single image super resolution (SISR) is the process that obtains a high resolution image from a single low resolution (LR) image by increasing the high frequency information and removing the degradation of the noise. Sparse representation of signal assumes linear combinations of a few atoms from a pre -specified dictionary. Sparse representation has been used successfully as a prior in signal reconstruction. Dictionary design is crucial for the success of reconstruction high resolution images. This paper evaluates the performance of dictionary design models in both mathematical and learning based models, it also compares the wavelet method, Haar method, DCT method, MOD method and K-SVD method. Various experiments are conducted using a real SPOT-4 satellite image. Experimental results demonstrate that the learning based approaches are very effective in increasing resolution and compare favorably to mathematical based approaches.
Keywords :
"Image resolution","Signal resolution","Dictionaries","IP networks","Image coding","Discrete cosine transforms","Encoding"
Publisher :
ieee
Conference_Titel :
Intelligent Computing and Information Systems (ICICIS), 2015 IEEE Seventh International Conference on
Print_ISBN :
978-1-5090-1949-6
Type :
conf
DOI :
10.1109/IntelCIS.2015.7397249
Filename :
7397249
Link To Document :
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