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dc.contributor.authorKainth, Raunaq Singh
dc.date.accessioned2018-06-26T11:38:25Z
dc.date.available2018-06-26T11:38:25Z
dc.identifier.urihttp://hdl.handle.net/10222/73981
dc.description.abstractSuper resolution image can be obtained from combining several low resolution noisy and blurred images. We propose an efficient algorithm to produce super resolution microscopic images. In the proposed algorithm, accurate sub-pixel motion between images is essential for reconstructing the image. Denoising is carried initially by adjusting the low resolution images. Shift fusion approach is applied to enhance the resolution of image and improved optical flow method is used for registration of images. The proposed method is applied to each color channel separately. The results are tested with synthetic downgraded images, popular low resolution datasets and experimental real-life images showing significant improvement in quality of images, with considerable less time cost and memory consumption than those of existing methods. Qualitative analysis is studied through edge detection method and observing visible features. Quantitative analysis is inspected showing improvement in resolution by measuring observable minimum distance.en_US
dc.language.isoenen_US
dc.subjectimage processingen_US
dc.subjectsuper resolutionen_US
dc.subjectimage enhancementen_US
dc.titleComputationally Efficient Super Resolution Algorithmen_US
dc.date.defence2016-04-27
dc.contributor.departmentDepartment of Electrical & Computer Engineeringen_US
dc.contributor.degreeMaster of Applied Scienceen_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorDr. Jason Guen_US
dc.contributor.thesis-readerDr. Kamal El-Sankaryen_US
dc.contributor.thesis-readerDr. William Phillipsen_US
dc.contributor.thesis-supervisorDr. Michael Cadaen_US
dc.contributor.thesis-supervisorDr. Alan Fineen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.copyright-releaseNot Applicableen_US
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