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I'm really surprised by double-shuffling can actually be solved. https://github.com/robinhouston/image-unshredding/#double-sh... It looks very unintuitive.
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Isn't the definition of insanity something about doing the same thing and expecting different results.
Funny, I thought more people would have gotten what I was saying here.
Now I'd like to see this run on a more realisticly shredded image. A real paper shredder creates strips that are more than one pixel thick are not straight on (I.e. the pixels don't necessarily align with the cuts) and possibly have cutting defects such as ragged edges or nicks.
I'm really surprised by double-shuffling can actually be solved. https://github.com/robinhouston/image-unshredding/#double-sh... It looks very unintuitive.
I wouldn't be surprised if a complete shuffling could be solved — i.e., every pixel is given a new random location. (Source: I do research involving stuff like this.)
I guess this could be used to reassemble images corrupted in other ways (other than shuffling). I have a disk image of jpegs and other data I'd like recovered. It was Windows formatted a few different ways but any blocks that format didn't touch are still there. There's other data there of course as well, so there would be lot of blocks that aren't part of images so would need to be ignored when reassembling the imag…
That said, I'm sure I could be surprised by what is possible.
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the important bit is you have to unscramble in reverse order of the scrambling. Unscrambling the columns and then unscrambling the rows would lead to smooth gibberish.
No. Column scrambling doesn't affect row ordering and row scrambling doesn't affect column ordering. They are literally orthogonal. You can't tell, from a scrambled matrix, whether the rows or the columns were scrambled first. Here's a more explicit breakdown: (0,0) (1,0) (2,0) (0,1) (1,1) (2,1) (0,2) (1,2) (2,2) Column scrambling: swap 1&2: (0,0) (2,0) (1,0) (0,1) (2,1) (1,1) (0,2) (2,2) (1,2) Now row scrambling: sw…
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I wouldn't be surprised if a complete shuffling could be solved — i.e., every pixel is given a new random location. (Source: I do research involving stuff like this.)
How could that be possible? Wouldn't that literally throw away everything but the distribution of colors in the image?
Earlier quoted context omitted.
I wouldn't be surprised if a complete shuffling could be solved — i.e., every pixel is given a new random location. (Source: I do research involving stuff like this.)
How could that be possible? Wouldn't that literally throw away everything but the distribution of colors in the image?
Do you know of any image compression technique that would drop information that we expect to be redeemable in a reasonable time using a solver of this sort?