This study intends to establish a sound testing and evaluation methodology based upon the human visual characteristics
for appreciating the image restoration accuracy; in addition to comparing the subjective results with predictions by some
objective evaluation methods. In total, six different super resolution (SR) algorithms - such as iterative back-projection
(IBP), robust SR, maximum a posteriori (MAP), projections onto convex sets (POCS), a non-uniform interpolation, and
frequency domain approach - were selected. The performance comparison between the SR algorithms in terms of their
restoration accuracy was carried out through both subjectively and objectively. The former methodology relies upon the
paired comparison method that involves the simultaneous scaling of two stimuli with respect to image restoration
accuracy. For the latter, both conventional image quality metrics and color difference methods are implemented.
Consequently, POCS and a non-uniform interpolation outperformed the others for an ideal situation, while restoration
based methods appear more accurate to the HR image in a real world case where any prior information about the blur
kernel is remained unknown. However, the noise-added-image could not be restored successfully by any of those
methods. The latest International Commission on Illumination (CIE) standard color difference equation CIEDE2000 was
found to predict the subjective results accurately and outperformed conventional methods for evaluating the restoration
accuracy of those SR algorithms.