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Learning to See in the Dark (2018)

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Re: Learning to See in the Dark (2018)

#101

Post by MFogleman »

numbernine wrote: post #70
echelon wrote: post #45

Earlier quoted context omitted.

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...

I'm not from Europe, but if I saw "BBC converts night images to perfect daylight in ~1 second", I would assume it meant the British Broadcasting Corporation. CNN is just as big of a name. His point is absolutely relevant.

Said differently: The percentage of people who are not from the US - but are aware of CNN as the Cable News Network, is higher than the percentage of people who are not machine learning experts - but are aware of CNN as a Convolutional Neural Network

Re: Learning to See in the Dark (2018)

#102

Post by anigbrowl »

echelon wrote: post #45
wyldfire wrote: post #14

Earlier 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.

I don't think it needs to be changed. I also read it as the news channel, wondered how that would make sense, and guessed it was the other sort of CNN. It's confusing but not misleading.

Re: Learning to See in the Dark (2018)

#103

Post by Rebelgecko »

numbernine wrote: post #70
echelon wrote: post #45

Earlier quoted context omitted.

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...

1. Not everyone works with ML ornis familiar with different types of neural nets

2. CNN exists outside of the US

Re: Learning to See in the Dark (2018)

#104

Post by BubRoss »

sdenton4 wrote: post #38
f- wrote: post #18

As 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,…

Your example strikes me as the kind of thing neural networks are much better at than a fixed filter. You or I could easily identify regions of an image where it's safe vs unsafe to do the surface averaging, and boundaries where we wouldn't want to mix up the averages. (For example, averaging text should be fine, so long as you don't cross the text boundaries.) A CNN should also be able to learn to do this pretty easi…

What you are describing is a class of filters known as edge preserving filters. You can look at bilateral filters and guided filters for examples that have been around for decades at this point.

Re: Learning to See in the Dark (2018)

#105

Post by yingw787 »

GEBBL wrote: post #3

Impressive of the American news channel, CNN, to convert images in minus one second.

Yeah, my mind jumped to CNN's coverage of the first Gulf War and having some color night vision for combat journalism:

https://www.thedrive.com/the-war-zone/25803/this-is-what-col...

Re: Learning to See in the Dark (2018)

#106

Post by anigbrowl »

jameshart wrote: post #41

The 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.

It's more than 'good enough' for most purposes. Matching other shots for Hollywood quality, probably not. For surveillance or the like it's fine. The things it's guessing poorly about are textures or colors.

Being able to read the title on the books in the example photo is great; you could rely on the title for evidentiary purposes, the smaller text probably not so much. So for a security camera it would do poorly at identifying the color of a car, but might well be sufficient to read the license plate.

Re: Learning to See in the Dark (2018)

#107

Post by [deleted] »

hombre_fatal wrote: post #80
echelon wrote: post #45

Earlier quoted context omitted.

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.

People will have the intellectual curiosity to click in to see what the title means anyways, I don't see how this is a problem in need of a fix.

[deleted]

Re: Learning to See in the Dark (2018)

#109

Post by Aardwolf »

I doubt this image is showing the true raw data (a):

https://github.com/cchen156/Learning-to-See-in-the-Dark/blob...

If you take the dark image (a) from that and balance its color, the information that is present in it simply cannot contain the text from the book covers and so on. In fact, it's full of JPEG artifacts despite the image being a PNG. It would be useful if they presented a histogram equalized image of (a).

Re: Learning to See in the Dark (2018)

#110

Post by anigbrowl »

selectodude wrote: post #61
jrimbault wrote: post #5

Why is the "page suspended" ? http://cchen156.web.engr.illinois.edu/paper/18CVPR_SID.pdf

The State of Illinois is out of money again.

You shouldn't be downvoted - with a big recession/depression looming, link rot and many sorts of repositories shutting down are a big issue that will slow down the pace of research.
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