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Study of Image Super Resolution Algorithms

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dc.contributor.author  Bouraba Nour El Houda  Rouabeh Lamia
dc.date.accessioned 2022-11-22T09:35:17Z
dc.date.available 2022-11-22T09:35:17Z
dc.date.issued 2022-09-22
dc.identifier.uri https://dspace.univ-bba.dz:443/xmlui/handle/123456789/2740
dc.description.abstract Super image resolution (SR) is a group of image processing technologies used in computer vision to improuve the resolution of deteriored images. Deep learning approaches have made great progress in super image resolution in recent years. In this study, we to provide a regular overview of current improuvments in image super resolution techniques using deep learning methodologies. Namely, we will describe and implement three SR algorithms : SRCNN, SRGAN and CAR. The comparative srudy is done in terms of the computation of two critera peak signal to noise ratio (PSNR) and the structure similarity index (SSIM). The obtained results have demonstrate the efficiency of the three amgorithms especialy CAR algorithm en_US
dc.language.iso fr en_US
dc.publisher faculté des sciences et de la technologie univ bba en_US
dc.relation.ispartofseries ;EL/M/2022/65
dc.title Study of Image Super Resolution Algorithms en_US
dc.type Thesis en_US


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