What is Reddit's opinion of SmartDeblur?

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http://i.imgur.com/ZCp1tFA.jpg?1

I tried different things for about an hour with this app. http://smartdeblur.net/tutorial.html

Blur size = 111 * (Medium) Smoothing = 88 * Aggressive Detection *

I find myself at a loss for words but I hope this brings you some joy.

You might be surprised how much you can deblur stuff:

http://smartdeblur.net/

It made it possible for me to read the variety of this particular line of product where I couldn't before... though some people probably could have anyway.

Smart Deblur can do this with a bit of fiddling. Blurity is another similar program.

You might be able to get better results if you speed more than 1 minute with it. The tricky part is that the motion is that they are both moving differently so the blur changes over the image.

It's true that certain types of blur can be reversed (e.g. motion blur, focal blur to an extent, and gaussian blur) with a lot of processing power, and they usually result in lots of artefacts. But definitely not all types or blur of any size, especially if it's non-uniform.

And "software not available to the public" is obviously bullshit, it's 2017.

There is a tool that supposedly can remove some kind of blur: http://smartdeblur.net/
I didn't check it, but if the pictures are not faked, it's somewhat impressive.
Of course if you blur to the point where all your selection in the image is a single solid color, you're pretty safe :D

Motion blur can be removed from photos.

Examples: http://smartdeblur.net/

There is still data contained in the blurred pixels. The data is simply smeared over a larger area. When it's globally blurred in a known direction it can be extracted.

I just tried out the Githup Repo. It's quite interesting, but will be difficult to use for video perhaps, since it doesn't seem to be able to save the "blur trace" thingy. But maybe that's just the "home" version... or whatever.

It also gives a link to a much newer version: http://smartdeblur.net

Check it out. It's quite good in my opinion. Of course it's no magic, but it gives about as much information back as I would think it should ...

In this specific case I'd just go there with Street View...

So if it's a sign in a location you know you could try that. You could try SmartDeblur, but it's unlikely to be "CSIable".

How is it better, specifically, than other deconvolution software?

Deconvolve (free)

Image Analyzer (free)

Smart Deblur (free trial)

Actually, pretty true, but not entirely, things like motion blur and some some other types can be corrected for without having to resort to guessing, or resulting in something random. What it's not going to help with is adding resolution to low resolution images, which is probably the case here, but the technology is there for making out faces and car number plates in blurry photos.

Have a look at Smart Deblur for some examples of what can be done and a free trial.

You're welcome.

Sharpness is limited by wavelength (i. e. diffraction), a fixed quantity, so building a kilometer-size pinhole camera would give extremely sharp images (once scaled down to manageable size). There were some experiments with rooms (fixed) or trucks (mobile) used for pinhole photography. E. g. here: https://www.vice.com/en/article/d74mkq/truck-sized-pinhole-camera-captures-american-panorama

For the body cap images you could try improving the sharpness through deconvolution with Piccure (seems unavailable right now) or Smart Deblur (Portableapps.com has an apparently free version).

You could also try recycling an old view camera from a thrift store if it comes with plate holders – but that wouldn't be digital anymore. ;-)

Have fun!

I have yet to see examples where ML sharpening looks like actual deconvolution. Proper deconvoltuion can, say, infer that one blurry cricle is actually letter "O" with hole in center and another is solid circle, and one pair of blobs is "8" and the another is "B" -- that it is shows details where human couldn't guess they were, not just output image looked sharper. Try, e.g. using using ML sharpening on examples from http://smartdeblur.net/

(note, that of course, hybrid approach ML supervising deconvolution, with noise reduction in intermediate stages, would be better).

>Yeah, but photon noise is at least a few orders of magnitude less than optical resolution in normal optical devices,

That's not even apples vs. oranges comparison. And the answer is no. By default, user is shown a quite processed image with noise reduction (even if luma noise NR is 0, chroma NR is always on by default). Open picture in dcraw in "documental mode" without any NR and you'll see a lot of noise.

>takes an end-run around actual deconvolution, Most methods of deconvolution consist of (repeatedly) convoluting input with "reverse PSF" or its approximation. Most used NNs today are convolutional NNs, so I guess it's easy to design NN which is a superset of some older deconvolution method. Also MLs can be useful for discriminating if settings are good for this particular image.

>he model ought to then be able to in a sense learn to do deconvolution on similar unseen objects

or different unseen objects, but photographed with same lens (=same PSF)

>while being agnostic as to the actual convolution function.

half-joke: show me ML which does RSA (or interger factorization) while being agnostic to process itself...

There is such a thing as unblur. Here's an article about it. Smartdeblur does what the author describes mathematically. (I don't own it.)

That being said, some enhancements are possible, e.g. removing motion blur from a shaky photo or generally deblurring. While the result isn't perfect, it's often enough to make out details you couldn't see before. Example.