Recent comments in /f/askscience

david-z-for-mayor t1_j9dvrrs wrote

Reply to How many colors can bioluminescence make? by Aximi1l

Let's divide your question into two parts: what colors can bioluminescence produce and what colors can humans see?

Since I'm more knowledgeable about humans, we will start there.

Let's start with a definition of light. Light is electromagnetic radiation that people can see. People can see radiation from about 400 to 700 nanometers in wavelength.

Most people have three types of color sensors in their eyes, commonly called cones. We have sensors for red, green, and blue light. When our cones sense light they send signals to the visual cortex part of the brain. Through the magic of neural processing, light signals are turned into a great many different colors. People are able to distinguish literally millions of colors as long as those colors are displayed as large uniform patches. When the color patches are small, we can't distinguish colors nearly as well. When it gets dark, we lose the ability to distinguish colors and everything fades toward grey or black.

Here's some of our neural magic: carefully adding red, green, and blue lights together makes white. Red and blue light make magenta, that makes sense. But red and green lights make yellow.

You wrote that bioluminesce can produce blue, green, and red light. If this light can be uniformly mixed over say a couple of square inches, then bioluminesce could produce millions of colors and indeed every color visible by people.

My little discourse talked about additive color mixing or what happens when you mix lights of different colors together as occurs on a computer monitor. There is also a process known as subtractive color mixing when pigments are mixed together as in printing or painting. Each pigment subtracts some wavelengths of light from what we see. In the subtractive process, people typically use 4 primaries: cyan, magenta, yellow, and black. Strictly speaking, black as a primary is redundant but it is cheaper to use black ink than a muddy mix of the other primaries to create black.

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dmmaus t1_j9du3dw wrote

Reply to Why can’t you “un-blur” a blurred image? by so-gold

> Let’s say you take a photo and then digitally blur it in photoshop. The only possible image that could’ve created the new blurred image is your original photo right?

No, that's not correct. Many different images could give you the same blurred image.

When you blur an image, fine detail below a certain scale is lost. If two images are the same at large scales, but different in the fine details below the scale where the blur filter removes them, and you blur them, you won't be able to tell the difference. So you can't decide which of the two images you started with. You can make a guess, but given there are infinitely many images that will blur to the same blurred result, you are likely to be wrong.

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StrictCommon388 t1_j9du2ql wrote

Reply to What is structurally stronger; a sphere or a cube? by [deleted]

How are these cubes and spheres made? Are they hollow or solid? What is the loading condition (point load vs distributed)? What is your criteria for stronger (first to yield at any point vs least overall deformation regardless of yielded materiel vs some other criteria)?

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SlingyRopert t1_j9dtray wrote

Reply to Why can’t you “un-blur” a blurred image? by so-gold

Unblurring an image is conceptually similar to the following story problem:

Bob says he has three numbers you don’t know. He tells you the sum of the numbers is thirty-four and that all of the numbers are positive. Your job is to figure out what those the numbers are based on the given information. You can’t really. You can make clever guesses about what the numbers might be based on assumptions, but there isn’t a way to know for sure unless you get additional information. In this example, thirty four represents the image your camera gives you and the unknown numbers represent the unblurred image.

In practice, there is a continuum of situations between images that can’t be unblurred and images that can be usefully improved. The determining factor is usually the “transfer function” or Linear translation invariant representation of the blurring operator applied to the image. If the transfer function is zero or less than 5% of unity at some spatial frequencies, the portions of the image information at these spatial frequencies and above is probably not salvageable unless you make big assumptions.

An equation called the Wiener filter can help you figure out which spatial frequencies of an image are salvageable and can be unblurred in a minimum squared error sense. The key to whether a spatial frequency can be salvaged is the ratio of the amount of signal (after being cut by the transfer function of the blur) to the amount of noise at that same spatial frequency.

When the signal to noise approaches one to one, you have to give up on unblurring that spatial frequency in the Wiener filter / unbiased mean squared error sense because there is no information left. This loss of information is what prevents unbiased deblurring.

If you are ok with having “biased” solutions and making some “big assumptions” you can often do magic though. For instance, you could assume that the image is of something that you have seen before and search a dictionary of potential images to see which one would (after blurring) look the most like the image you received from the camera. If you find something whose blurred image matches you could assume that the unblurred corresponding image is what you imaged and nobody could prove you wrong given the blurry picture you have. This is similar to what machine learning algorithms do to unblur an image by relying on statistical priors and training. You run the risk with this sort of extrapolation that the resulting unblurred image is a bit fictitious.

I personally recommend being cautious with unblurring using biased estimators due to the risk of fictitious imagery output.

It is always best to address the blur directly and make sure that you don’t apply a blur so strong that the transfer function goes to near zero.

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Lilweedoholic t1_j9dsd5p wrote

Reply to How and why does asphyxiation induce euphoria? by Ausoge

Asphyxiation makes you feel good because it lowers oxygen in the brain and releases happy chemicals. But asphyxiation is very dangerous and can be deadly. There's no proof that it's an evolutionary adaptation, so it's not a good idea to try it. It's better to avoid practices that cause asphyxiation.

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