A Robust quality metric for image super resolution /

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2015
Kipman, Yiğit
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Superresolution have become an active topic in image processing in the last decade. Various superresolution algorithms have been developed; however these superresolution algorithms may introduce defects such as blurring, aliasing, added noise and ringing. Evaluating the performance of these superresolution algorithms is an important problem; because the original high resolution image is not available while quantifying the quality of superresolution image. Subjective tests can be made to quantify the perceived image quality; but they are time-consuming and expensive. Only a few objective quality ssessment algorithms are proposed that evaluate the quality of superresoluted image from its low-resolution (LR) pair; but these do not correlate well with the subjective tests. In this thesis, a quality assessment algorithm for image superresolution that follows the philosophy of natural scene statistics (NSS) is analyzed and an improvement is proposed. A statistical model of frequency energy falloff characteristics of high resolution (HR) images is developed and a quality measure is calculated from the departures from HR image statistics. A no-reference spatial image quality assesment measure that also follows the philosophy of NSS is incorporated in the proposed algorithm to improve the robustness of the metric against noise. It is shown that the proposed approach is robust against noise and correlates well with the human visual system.
Image processing., Image processing, Imaging systems
http://etd.lib.metu.edu.tr/upload/12618516/index.pdf
https://hdl.handle.net/11511/24443
Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats
Y. Kipman, “A Robust quality metric for image super resolution /,” M.S. - Master of Science, Middle East Technical University, 2015.
METU IIS - Integrated Information Service open@metu.edu.tr