Authors :
Harshada Haridas Khatavkar , Dr. Anilkumar Holambe
Volume/Issue :
Volume 2 - 2017, Issue 8 - August
Google Scholar :
https://goo.gl/bEVkwb
Scribd :
https://goo.gl/ZonU3E
Thomson Reuters ResearcherID :
https://goo.gl/3bkzwv
Abstract :
The Super Resolution (SR) reconstruction has become a hot research topic in the field of Image Processing. Super resolution is all about generating high-resolution image from low-resolution image. High resolution image provides a high pixel density therefore provides more information about the original image. High resolution images are very much important for computer vision applications for better performance for pattern recognition and analysis of images. It is useful in medical imaging for diagnosis. It is very much useful for processing of satellite images. Also it is useful for other applications. In this paper we have discussed the techniques used for obtaining super resolutions. It is implemented using MATLAB.
Keywords :
MATLAB, Sparse
The Super Resolution (SR) reconstruction has become a hot research topic in the field of Image Processing. Super resolution is all about generating high-resolution image from low-resolution image. High resolution image provides a high pixel density therefore provides more information about the original image. High resolution images are very much important for computer vision applications for better performance for pattern recognition and analysis of images. It is useful in medical imaging for diagnosis. It is very much useful for processing of satellite images. Also it is useful for other applications. In this paper we have discussed the techniques used for obtaining super resolutions. It is implemented using MATLAB.
Keywords :
MATLAB, Sparse
