91–100 of 179 posts
Earlier quoted context omitted.
Well, Turner used to colorize b/w movies, I guess progress marches on.
Well I got the right CNN by the time I got to the end of the title, but, "Turner"? The British pastoral landscape painter? You lost me.
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...
Agree that title should be changed.
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,…
I'm sure you know exactly how much of which filter to apply for similar results. Laymen like ourselves will need a lot more trial and error. Their contribution here is to provide a push-button, automated mechanism.
I would have probably also tried something simple and given up due to the noise. So this is definitely interesting.
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…
Could something like this be done for night vision goggles or is there significant latency?
Earlier quoted context omitted.
They should make a CLI tool, it stores your processed images one sec before it is invoked! See also: https://en.m.wikipedia.org/wiki/Thiotimoline The major peculiarity of the chemical is its "endochronicity": it starts dissolving before it makes contact with water.
See also Ted Chiang's What’s Expected of Us, where there's a device with a button and a LED, and the LED always lights up one second before you press the button.
The heart of each Predictor is a circuit with a negative time delay — it sends a signal back in time.
What camera are they shooting at 409,600 ISO at?
In the video they reference the Sony A7S II, on Sony's website[1] they claim: >Still images: ISO 100-102400 (expandable to ISO 50-409600), [1]: https://www.sony.co.uk/electronics/interchangeable-lens-came...
So in this instance they're processing lossily on top of an image already processed lossily in-camera.
Earlier quoted context omitted.
Counterpoint: The human brain converting a 2D image to a 3D model is educated-guesswork too :)
This hits on an interesting point. There is an entropy limit to the message, but the message isn't actually the only data. One thing humans are great at is integrating existing knowledge into a messy situation and intuiting more than is available just from the raw message. I.e. The message has an entropy limit, but the message isn't the whole dataset.
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,…
The comparison is fair because it tries to automate expertise. I'm sure you know exactly how much of which filter to apply for similar results. Laymen like ourselves will need a lot more trial and error. Their contribution here is to provide a push-button, automated mechanism. I would have probably also tried something simple and given up due to the noise. So this is definitely interesting.
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 you would get from trying to increase brightness in a trivial way.