To clarify the difference between sharpening, deblurring, and super resolution, I will use another example programmed in MATLAB.
When an image (or video frame) is downscaled two things happen: the image is blurred and compressed. Blurring causes the information content to be distributed differently among the pixels, resulting in loss of detail, but in principle no information is lost. Compression results in loss of information. We can subsequently upscale the downscaled image back to its original size through interpolation. When we compare the upscale to the original, we obviously get a blurred image with less detail:
Sharpening is the same as applying an unsharp mask filter. An unsharp mask filter enhances the high frequency content in the image. In other words sharpening results in edge enhancement. No lost details are recovered, the existing details are simply made more visible:
A side effect of sharpening is ringing. Although sharpening enhances detail, it does not necessarily result in a better representation of the original high res image. It simply represents a subjective reimagining of the low resolution image.
Deblurring is the process of undoing the effects of blurring. Provided a reasonable estimate of the blur function is known, many details can be recovered:
The following comparison shows the difference between sharpening and deblurring. The deblurred image obviously has more detail, and is a better representation of the high res image. The deblurred image also has less artifacts, such as reduced ringing:
However, the loss of detail due to compression cannot be undone by deblurring. If we have multiple images with subpixel shifts the images can be super resolved, and part of the detail lost due to compression can be recovered. The objects in the images are registered, aligned, averaged, and deblurred:
The super resolved image has more (accurate) detail, less artifacts, such as reduced ringing, and is an even better representation of the high res image, when compared to the deblurred single image:
Summarizing, sharpening enhances existing details, deblurring reconstructs details lost as a result of blurring, super resolution reconstructs details lost as a result of blurring and compression. Any of these methods results in artifacts, the foremost being ringing. However, sharpening generally has the most artifacts, while super resolution has the least.
The sucess of super resolution in practise, hinges on accurate data fusion, and the accuracy of the estimated point spread function (blur function). Over the last decades increasingly accurate approximate solutions have been defined for both these problems, resulting in accurate and patented applications in image/video processing and forensics.
For more information on the limits of super resolution, I refer you to this scientific paper: