Why is the "page suspended" ? http://cchen156.web.engr.illinois.edu/paper/18CVPR_SID.pdf
The State of Illinois is out of money again.
Learning to See in the Dark (2018)
61–70 of 179 posts
Re: Learning to See in the Dark (2018)
#62As a photographer, the comparison to "raw" results without color balance or noise removal seems somewhat deceptive. The effects visible in the video seem easy to quickly replicate with existing techniques, such as the "surface blur" filter that averages out pixel values in areas with similar color. This happens at the expense of detail in low-contrast areas, producing a plastic-like appearance of human skin and hair,…
i always have this complaint too. its fundamentally a lossy process, in the hand wavy sense. its more "impressive" looking, but actually conveying less real detail.
Re: Learning to See in the Dark (2018)
#63I was just wondering a couple days ago why the image from my phone is so grainy, while my eyes+brain can see everything clear in the dark (it wasn't completely dark, of course). This seems to replicate the post-processing we do in our brain (which is also a giant neural network). I wonder if the process is similar?
Very small numbers of photons (1) are required to trigger rhodopsin cycle. So primary receptor itself is very very VERY sensitive.
That's actually a very cool fact:
https://www.nature.com/news/people-can-sense-single-photons-...
Re: Learning to See in the Dark (2018)
#64Impressive of the American news channel, CNN, to convert images in minus one second.
I understand that the title may have brought confusion, and I think your comment calls attention to this whilst also being somewhat funny. But still, could we make an effort not to devolve into what has happened on Reddit, i.e. comment sections which mainly consist of puns and other low effort jokes?
I don't understand what you are saying, it is very "HN" to comment on the title and not on the article.
And it is even more "HN" to comment on details of the title or the article because you don't really know what to say about the article.
Look at my comment.
Re: Learning to See in the Dark (2018)
#65The problem with techniques like this is that they fundamentally amount to ‘making a plausible guess as to what the image would look like’, since essentially they can’t extract information that is simply not there. There is a Shannon entropy limit here. Machine learning is really machine-enhanced educated-guesswork, which has its place but also has its limits.
I'm also not a fan of how the only part actually readable in the (a) original, which is part of the title in the front book, becomes completely whitewashed in (c). Where the model actually had the most information, it completely removed it in the result...
> the model actually had [...] information
Wait, did it? Isn't the middle photo being shown for comparison only, rather than as an input?
Re: Learning to See in the Dark (2018)
#66[deleted]
Re: Learning to See in the Dark (2018)
#67isn't this just some tweaked raw processing algorithm?
Re: Learning to See in the Dark (2018)
#68Re: Learning to See in the Dark (2018)
#69Earlier quoted context omitted.
I'm also not a fan of how the only part actually readable in the (a) original, which is part of the title in the front book, becomes completely whitewashed in (c). Where the model actually had the most information, it completely removed it in the result...
> the model actually had [...] information Wait, did it? Isn't the middle photo being shown for comparison only, rather than as an input?
I guess I might have misinterpreted the goal. If the goal was to make the image look like it was daylight, then maybe whitewashing that light reflection was the correct choice. If the goal was to "see in the dark", then it seems like a very bad choice.
EDIT: Finally got the paper to load via the helpful wayback machine link provided in another thread. It looks like the goal was to simulate a long exposure with a short exposure. So whitewashing of "bright" areas in the original might be expected.
Re: Learning to See in the Dark (2018)
#70Earlier quoted context omitted.
CNN here is a "Convolutional Neural Network"
The title needs to be changed so brains recognize it as such. It either needs a preceding adjective or letter indicating what type of convolutional network it is. The other option is spelling it out. Most people will read CNN as the news channel. Even those familiar with neural networks.
Not everyone is from the US...