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Learning to See in the Dark (2018)

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Re: Learning to See in the Dark (2018)

#141

Post by y7r4m »

Hi, I'm a developer at NexOptic[0] and we are a company that was deeply inspired by this paper when it was first published. We had a lot of early success when attempting to replicate the results on our own and ended up running with it, and extending it into our own product line under our ALIIS brand of AI powered solutions.

For those curious, our current approach differs in some very significant ways to the author's implementation, such as performing our denoising and enhancement on a raw bayer -> raw bayer basis with a separate pipeline for tone mapping, white-balance, and HDR enhancement. As well, we explored a fair amount of different architectures for the CNN and came to the conclusion that a heavily mixed multi-resolution layering solution produces superior results.

As other commentators have pointed out, the most interesting part of it is really coming to terms that, as war1025 pointed out, "The message has an entropy limit, but the message isn't the whole dataset." It is incredibly powerful what can be accomplished with even extraordinarily noisy information as long as one has a extremely "knowledge packed" prior.

If anyone has any questions about our research in this space, please feel free to ask.

[0] https://nexoptic.com/artificialintelligence/

Re: Learning to See in the Dark (2018)

#142

Post by ggregoire »

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.

The new title is even worst tho. "Learning to See in the Dark" gives absolutely no clue on what's the article is about.

Re: Learning to See in the Dark (2018)

#144

Post by BubRoss »

pera wrote: post #127
BubRoss wrote: post #100

Earlier quoted context omitted.

This is completely untrue. What you are describing is usually called automatic tone mapping. This is basically noise reduction and possibly color normalization from brightening a dark image. Them showing their black image as the starting point is silly, because jpg will make a mess of the remaining information. What they should show is the raw image brightened by a straight multiplier to show the noisy version that y…

What jpg? they are using raw data.

Their example on their github page uses a jpg that makes it look like they are creating something from nothing.

Re: Learning to See in the Dark (2018)

#146

Post by Groxx »

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.

And you can see some of its biases in the results, yeah. Look at the thick book's spine on the right, and compare the box around the title - "our result" has pretty significant staircasing instead of being a slightly-off-vertical line.

Re: Learning to See in the Dark (2018)

#147

Post by pachico »

Funny. I'm walking down the corridor almost in total darkness trying to get my son to sleep. I get bored and with my free hand reach to my phone, open NH and stumble upon this title. Totally unrelated to its content but I had a (quiet) laugh :)

Re: Learning to See in the Dark (2018)

#148

Post by [deleted] »

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,…

[deleted]

Re: Learning to See in the Dark (2018)

#149

Post by throwlaplace »

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.

>‘making a plausible guess as to what the image would look like’

people bring this up all the time as hot-takes in these areas. it's conditional inference. it's no more disingenuous than linear regression.

Re: Learning to See in the Dark (2018)

#150

Post by dbeardsl »

y7r4m wrote: post #141

Hi, I'm a developer at NexOptic[0] and we are a company that was deeply inspired by this paper when it was first published. We had a lot of early success when attempting to replicate the results on our own and ended up running with it, and extending it into our own product line under our ALIIS brand of AI powered solutions. For those curious, our current approach differs in some very significant ways to the author's…

Sounds like you have taken this pretty far, do you have any example outputs? The only one I found via your website was a PDF with a low res image with no context.
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