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Thread: Recent advancement in image resolution enhancement

  1. #11

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    Re: Recent advancement in image resolution enhancement

    Quote Originally Posted by Nodda Duma View Post
    If you consider the "downsampling operator" is analogous to the optical point spread function and the Nyquist frequency for the imaging system, then you'll understand why this technique and others like it are not only theoretically possible, but practically possible as well.

    This isn't the only approach nor are they working in a vacuum. Super-resolution techniques have been actively employed for over a decade, seeing first widespread use in smart phone cameras.

    So yeah, it is a reality. Their research, like almost all research, is a small slice of ongoing incremental advancement in a field which the public conscious is only dimly aware of. So I have to chuckle at naysaying that sounds akin to explaining why this internet thing will never take off.

    If you think this is amazing technology, look at actual hot optical engineering research topics such as computational optics or plastic GRIN lens printing, graphene detector research, or laminated infrared optics.
    Is anyone saying that the technique is not possible, or an isolated effort? I think the naysaying is about how well it works, not that it does not exist. In context to the specific work, down-sampling is not very relevant to the objectives. What they are really after is an accurate up-sampling, or more aptly, interpolation of estimated, missing information. In a practical application, one would start with an image with deficient resolution and try to fill in more pixels with some information from neighboring pixels. I read the paper, and down-sampling and Nyquist frequency hardly gets at what they are doing. My take-away is that their main contribution was getting away from a pixel-wise mean-squared error approach that is commonly used but does not yield very accurate results. Their method is quite impressive, but still, I wonder if it would be good enough in say a court of law where someone's life depended on a truthful image reconstruction.

    The research is a reality, but the image reconstructions do not match reality, although they come close sometimes.

  2. #12
    Nodda Duma's Avatar
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    Re: Recent advancement in image resolution enhancement

    Like most papers on image enhancement going back 30 years or more, the authors start with a control image (commonly Lena's photo but not here), replicate the image degradation of real systems, and then run the degraded image through their algorithm to show how well they can replicate reality.

    I find it humorous that folks get hung up on the image degradation aspect which isn't the interesting or even meaningful part of the paper at all.
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  3. #13

    Re: Recent advancement in image resolution enhancement

    Thanks for adding some depth behind this area of research Nodda. Regardless of the necessary and healthy levels of skepticism, I suspect/hope most who work in the digital medium see it as an exciting possibility. What I appreciate about the present article, is their openness in including scripts so that readers can a) validate the authors results themselves, and b) apply their program for their own purposes. The scripts they provide are elegant in their simplicity, although I don't normally write in python. In comparison to R they are rather concise.

    Thanks mmerig for the suggested additional iterations for my tests. Sadly, it could be a month or two before I can complete this.

  4. #14

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    Re: Recent advancement in image resolution enhancement

    Quote Originally Posted by Nodda Duma View Post
    Like most papers on image enhancement going back 30 years or more, the authors start with a control image (commonly Lena's photo but not here), replicate the image degradation of real systems, and then run the degraded image through their algorithm to show how well they can replicate reality.

    I find it humorous that folks get hung up on the image degradation aspect which isn't the interesting or even meaningful part of the paper at all.
    If the downsampling is not meaningful (and I think it is, for reasons stated earlier), then why use it? That's a rhetorical question, like my other one about "Who would downgrade an image and then try to fix it". Sorry that my facetious nature did not come through in my message, or maybe that is why Nodda Duma found it funny. Also, I know Nodda Duma is a lens designer, and I am not.

    Sure, this image enhancement stuff is interesting, can be useful, and there is probably big money in it if Adobe, the FBI, etc. likes it, but as others mentioned, it's the validity aspect that can be overstated.

    In practical situations, a directionally uniform downsampling method, like bicubic, may not mimic some real-world image degradation from camera movement, or selective focus due to lack of depth of field or a focus mistake, etc. where different parts of the image would need more enhancement (or none) than others -- common things that people may want to fix, rather than a bicubic downsampled one. Surveillance images are an obvious example: they can be blurry from low resolution, signal noise, and subject movement. Maybe research has dealt with this in a big way, but given your message, it sounds like the "control image" approach has been standard practice for decades. It makes sense to use it as they do, but has anyone stepped out of this box to address some other practical image problems?

    Or, does bicubic smoothing mimic every kind of image degradation out there, and my concerns about its applicability are mis-placed? (This one is sort-of rhetorical).

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  5. #15
    Nodda Duma's Avatar
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    Re: Recent advancement in image resolution enhancement

    Bicubic sampling / smoothing isn't in the same category. That's an interpolative method.

    Super resolution techniques take advantage of the fact that real-world image degradations (think MTF) are predictable and can be removed by deconvolution. The methods in that category don't add what isn't there or guess via interpolation, but extracts what can be extracted out of the available information.

    Beyond that I can't dig further...it starts to become black magic even for me. The only other thing I can say is: You can see proof of the effectiveness of this type of approach in the imagery of the iPhone and other smart phones.
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