A Review Paper on Single Frame Super-Resolution and Image Quality Assessment

Abstract

Image quality assessment plays very important role in different image processing applications
such as image enhancement, image compression, image restoration, image acquisition and other fields.
Image quality assessment is necessary because images may contain different types of noise like blur, noise,
contrast change, etc. Image quality assessment researchers face many problems when designing a model of
Human Visual System which can deal with natural images. The latest progress on developing automatic IQA
methods that can predict subjective quality of visual signals is exhilarating. For example, a handful of
objective IQA measures have been shown to significantly and consistently outperform the widely adopted
mean squared error (MSE) and peak signal-to-noise-ratio (PSNR) in terms of correlations with subjective
quality evaluations. In this paper we have reviewed some papers based on IQA. There have been increasing
number of super-resolution(SR) algorithm proposed recently to create high-resolution(HR) images from lowresolution(LR) images. A great deal of effort has been made in recent years to develop objective image quality
metrics that correlate well with perceived human quality measurement or subjective methods. We have tried to
find some advantages of them by studying it.