I'm a little late to the party, but super-resolution is a topic of interest in computer vision, and this approach is one of many that have been recently proposed. At a high level, I should say that the field is still searching for the right approach, and there is by no means a consensus that this method (or more generally, this philosophy) is the right one.
As a general rule of thumb, the performance shown in example figures of a computer vision paper should be taken as best cases (and in some papers, as outliers), and even the "failures" shown are often not the worst or most typical. Similarly, quantitative results are generally as optimistic as possible, arrived at through "graduate student descent" of the parameter space.
So I think we are still quite far from a super-resolution method that is truly practical OR effective.
Some specific points about this paper:
- This research group has a long history of exploring methods based on exploiting self-similarity in images for various tasks (super-resolution, denoising, segmentation, etc.), and although they have shown remarkable progress on this front, it is generally agreed that using more than just a single image would improve results drastically for just about any task.
- The use of more-than-exhaustive self-similarity search is EXTREMELY expensive, however, and that's why runtime is often not mentioned in these papers. It's not uncommon for processing times to be on the order of DAYS, for a single tiny VGA image. (I don't remember whether they quote a number in this paper or not, but it's certainly not faster than a few hours per image, unless you start using some hacks.)
- As others have commented, this method is not actually extracting new information from the image, but rather "hallucinating" information based on similar parts of the image. There is certainly reasonable justification for its use as a prior, but it's not clear whether it's optimal, or even close to it.
- My gut feeling is that super resolution methods will find much more application in compression-related areas rather than CSI-esque forensics. For example, JPEG compression is roughly 30%. But downsample an image by 2x in each direction and your compression ratio is already 25%. So if you can downsample images a few times and then use super-resolution to "hallucinate" a high-resolution version, that's probably good enough for the vast majority of common images (e.g., facebook photos), where users aren't very sensitive to quantitative "correctness". And of course with mobile adoption happening much faster than mobile bandwidth is increasing, this becomes an ideal application domain.
As a general rule of thumb, the performance shown in example figures of a computer vision paper should be taken as best cases (and in some papers, as outliers), and even the "failures" shown are often not the worst or most typical. Similarly, quantitative results are generally as optimistic as possible, arrived at through "graduate student descent" of the parameter space.
So I think we are still quite far from a super-resolution method that is truly practical OR effective.
Some specific points about this paper:
- This research group has a long history of exploring methods based on exploiting self-similarity in images for various tasks (super-resolution, denoising, segmentation, etc.), and although they have shown remarkable progress on this front, it is generally agreed that using more than just a single image would improve results drastically for just about any task.
- The use of more-than-exhaustive self-similarity search is EXTREMELY expensive, however, and that's why runtime is often not mentioned in these papers. It's not uncommon for processing times to be on the order of DAYS, for a single tiny VGA image. (I don't remember whether they quote a number in this paper or not, but it's certainly not faster than a few hours per image, unless you start using some hacks.)
- As others have commented, this method is not actually extracting new information from the image, but rather "hallucinating" information based on similar parts of the image. There is certainly reasonable justification for its use as a prior, but it's not clear whether it's optimal, or even close to it.
- My gut feeling is that super resolution methods will find much more application in compression-related areas rather than CSI-esque forensics. For example, JPEG compression is roughly 30%. But downsample an image by 2x in each direction and your compression ratio is already 25%. So if you can downsample images a few times and then use super-resolution to "hallucinate" a high-resolution version, that's probably good enough for the vast majority of common images (e.g., facebook photos), where users aren't very sensitive to quantitative "correctness". And of course with mobile adoption happening much faster than mobile bandwidth is increasing, this becomes an ideal application domain.