飞机安全谎言_停止相信电视的谎言：关于“增强”图像的真实真相

飞机安全谎言

You’ve seen it over and over. The FBI uses their advanced technology to “enhance” a blurry image, and find a villain’s face in the worst possible footage. Well, How-To Geek is calling their bluff. Read on to see why.

您已经看过一遍遍了。 FBI使用其先进的技术来“增强”模糊的图像，并在可能的最差镜头中找到小人的脸。 好吧，How-To Geek称他们为虚张声势。 继续阅读以了解原因。

It’s one of the most common tropes in television and movies, but is there any possibility a government agency could really have the technology to find faces where there are only blurry pixels? We’ll make the argument that not only is it impossible with current technology, but it is very unlikely to ever be a technology we’ll ever see. Stick around to see us put this trope under the lenses of science and technology, and prove it wrong once and for all.

这是电视和电影中最常见的比喻之一，但是*机构真的有可能真正拥有这项技术来发现只有模糊像素的人脸吗？ 我们将提出这样的论据：不仅现有技术不可能实现，而且不可能成为我们将看到的技术。 坚守四周，看看我们将这支望远镜置于科学和技术的作用之下，并一劳永逸地证明它是错误的。

如何限制成像和光照证明所有图片 (How Imaging and Light Prove All Pictures are Limited)

All imaging technologies, either digital or analog, all work roughly the same way. Let’s think about cameras for a moment. All cameras create some kind of image when light (particles we call photons) interact with some sort of image creating media. In digital cameras, it is a photoelectric sensor. In film cameras, it is a chemically treated, light sensitive strip of film.

所有成像技术(无论是数字还是模拟)都以大致相同的方式工作。 让我们考虑一下相机。 当光(我们称为光子的粒子)与某种图像创建介质相互作用时，所有相机都会生成某种图像。 在数码相机中，它是一个光电传感器。 在胶卷相机中，它是经过化学处理的感光胶片带。

It may surprise you to know that film-based cameras can capture more detail than even extremely high resolution digital cameras. But even with a film camera, only a limited amount of light can be recorded on the film. The same is true with any imaging device, be it a video recorder, a digital camera, or a flatbed scanner. And since any picture is taken in a finite period of time (usually fractions of a second, in the case of cameras), there is necessarily an upper limit to the detail of any captured image.

知道基于胶片的相机甚至比极高分辨率的数码相机都能捕捉到更多细节，这可能会让您感到惊讶。 但是即使使用胶片相机，也只能在胶片上记录少量的光。 对于任何成像设备，无论是录像机，数码相机还是平板扫描仪，都是如此。 而且，由于任何图片都是在有限的时间段内拍摄(对于相机而言，通常是几分之一秒)，因此任何拍摄图像的细节都必然存在上限。

In digital imaging, that upper limit often has to do with the ceiling the camera or device has—the number of pixels the sensors inside the camera are capable of detecting, for instance. This is all about the limits of the device itself, and is slightly different than the problem of a finite amount of light reaching the media in the camera. Put simply, no camera, no matter how advanced, has an infinite capacity for resolution.

在数字成像中，上限通常与相机或设备的天花板有关-例如，相机内部的传感器能​​够检测的像素数量。 这完全是关于设备本身的限制，并且与有限的光到达相机中的介质的问题稍有不同。 简而言之，无论多么先进的相机，都没有无限的分辨率。

所有数据都是其他数据的产物-垃圾进，垃圾出 (All Data is a Product of Other Data–Garbage In, Garbage Out)

Computers are interesting machines, but they are not without their limitations. One of things most people misunderstand about computers is that they aren’t really capable of creating “new” information, they just sort of create “different” information. In mathematics, when one part of an equation is defendant on another part, it’s called a function. When Y = X+1, Y is a function of X. Whatever, X is, Y is directly correlated.

计算机是有趣的机器，但是它们并非没有局限性。 大多数人对计算机误解的一件事是，它们实际上并不能创建“新”信息，而只是创建“不同”信息。 在数学中，当方程的一个部分在另一部分上是被告时，则称为函数。 当Y = X + 1时，Y是X的函数。无论X是什么，Y都直接相关。

Computers operate in a similar manner. You can give a computer a huge text file of random letters and a dictionary, and tell it to arrange those limited set of letters into words from the dictionary. This works because the end product can be broken down into a function of the set of random letters, the words from the dictionary, and the directions to create one from another.

计算机以类似的方式运行。 您可以给计算机提供一个巨大的随机文件和字典的文本文件，并告诉计算机将这些有限的字母集排列成字典中的单词。 之所以可行，是因为可以将最终产品分解为一组随机字母，字典中的单词以及从中创建一个的方向。

Imagine you’re doing algebra homework on your computer. You plug in a series of numbers into your “Y = X+1” equation. First, X = 1, so 1 + 1 = 2. But what would happen if you pushed the wrong keys, and input the wrong numbers? Would you still get the correct answer? If you meant to say X = 1, but typed X = 11, would the computer still give you the correct answer? The question is, of course, preposterous. This is the concept of “Garbage In, Garbage Out.” In other words, the wrong data will give the wrong answer.

想象一下，您正在计算机上进行代数作业。 您将一系列数字插入“ Y = X + 1”方程式中。 首先，X = 1，所以1 +1 =2。但是，如果您按下错误的键并输入错误的数字会发生什么？ 您还会得到正确答案吗？ 如果您要说X = 1，但键入X = 11，计算机是否仍会为您提供正确答案？ 这个问题当然是荒谬的。 这就是“垃圾进，垃圾出”的概念。 换句话说，错误的数据将给出错误的答案。

Like our equation, “enhanced” images are a function of the original image. When you start with a blurry or pixelated image (or even a sharp clean one, for that matter) no amount of filters or computer magic can coax information out of a place where that the information simply doesn’t exist. Just as “1 + 11” will never result in “2,” a limited image will never result in the so-called “enhanced” version.

像我们的方程式一样，“增强”图像是原始图像的函数。 当您开始使用模糊或像素化的图像(或什至是清晰的图像)时，没有大量的滤镜或计算机魔术师可以将信息从根本不存在的地方哄骗出来。 正如“ 1 + 11”永远不会导致“ 2”一​​样，有限的图像也永远不会导致所谓的“增强”版本。

为什么没有从无到有创建数据的功能 (Why There’s No Function to Create Data from Nothing)

You might ask the question, “Isn’t is possible to create a function that can add detail to a bad image?” Well, we’re not likely to create one anytime soon. Simply because we recognize an arrangement of pixels as a face does not mean that it’s an actual face. The face part is our perception of that data—we are in fact only looking at data! To take image data and transform it into “better” data is an impossibility. A function that creates something as specific as a human face from nonsense data would require actual knowledge of the end product—you would need to know the actual person’s face in order to “find” it in the blurry image, which sort of defeats the point of this imaginary technology anyway.

您可能会问一个问题：“无法创建一个可以为不良图像添加细节的功能吗？” 好吧，我们不太可能很快创建一个。 仅仅因为我们将像素的排列识别为人脸并不意味着它是实际的人脸。 面部表情是我们对数据的感知-实际上，我们只是在看数据！ 拍摄图像数据并将其转换为“更好”的数据是不可能的。 一个从废话数据中创建出像人脸一样具体的东西的功能将需要最终产品的实际知识-您需要知道实际人的脸，才能在模糊的图像中“找到”它，这有点不合时宜。无论如何，这种虚构技术。

It may be possible to create some kind of face-like image from garbage image data, but this doesn’t mean that that product will be relevant. It might create a face that doesn’t actually look anything like the person that was actually there. It would more likely just create a mass of pixels that sort of just looks like a “different” version of what’s there. In TV logic, there’s a face locked behind that image, and the good guys are simply going to find a way to get to it. In reality, it’s only data—and any function that recreates the circumstances of a photo being shot already has that information within it.

可能可以从垃圾图像数据中创建某种类似人脸的图像，但这并不意味着该产品将是相关的。 它可能会创建一张看上去实际上并不像实际存在的人的面Kong。 更有可能只是创建大量的像素，看起来像是那里的“不同”版本。 在电视逻辑中，该图像后面有一张面Kong，好人只是想找到一种方法来实现它。 实际上，这仅仅是数据，并且任何能够重现拍摄照片的环境的功能都已经包含了这些信息。

如何秘密知道*没有做这不可能的事情 (How to Know The Government Secretly Isn’t Doing this Impossible Thing)

Lots of money is being spent by government agencies like NASA to search the sky with satellite telescopes like the Hubble and the Kepler. These scopes and others on earth provide amazing, deep space digital photography of light, and also other wavelengths in the electromagnetic spectrum—things like radio and micro waves, and high frequency radiation, like gamma and x-rays. But all of these images are subject to the same limitations discussed earlier. They are snapshots in time. A limited imaging of X-rays is the same as a limited imaging of visible light. If images could be “enhanced,” deep space photography would be easy for anyone and everyone to do. If you can “enhance” an image by zooming in on a face in a crowd, why not go outside, take a snapshot of the sky, and “enhance” it to see the details on the ground of Pluto? If this was possible, an image—any image—could conceivably contain all the image data in the universe.

美国国家航空航天局(NASA)等*机构正花费大量资金，使用哈勃(Hubble)和开普勒(Kepler)等卫星望远镜搜索天空。 这些示波器和地球上的其他示波器提供了令人惊叹的深空数字摄影，包括光以及电磁波谱中的其他波长，例如无线电波和微波波，以及诸如伽玛射线和X射线之类的高频辐射。 但是所有这些图像都受到前面讨论的相同限制。 它们是时间的快照。 X射线的有限成像与可见光的有限成像相同。 如果可以“增强”图像，那么任何人和任何人都可以轻松进行深空摄影。 如果您可以通过放大人群中的面部来“增强”图像，为什么不走到外面，拍摄天空快照，然后“增强”以查看冥王星地面的细节？ 如果可能的话，可以想象一个图像(任何图像)都可以包含Universe中的所有图像数据 。

实际有用的图像增强可能吗？ (Is Actual Useful Image Enhancement Possible?)

Simply because the way trope-riddled writing presents image enhancement is wrong, wrong, wrong, doesn’t mean that graphics programs aren’t useful tools for this sort of problem. As long as the information is actually within the image, some sort of “enhancement” might make it easier to see. Take, for instance, this dark, shadowed image, lightened to show detail within the shadow. This type of “enhancement” is real, and available to anyone with a computer. The difference is the data is already there—we’re just looking at it a different way. Our eyes can’t see (depending on your monitor) the detail in the face on the left. But the “enhanced” version on the right shows us plenty of detail in the shadow, giving us a better picture of his face.

仅仅因为用中规中矩的文字来表现图像增强的方法是错误的，错误的，错误的，并不意味着图形程序不是解决此类问题的有用工具。 只要信息实际上在图像中，某种“增强”可能会使它更容易看到。 例如，以这张深色阴影的图像变亮以显示阴影中的细节。 这种类型的“增强”是真实的，任何有计算机的人都可以使用。 区别在于数据已经存在，我们只是以不同的方式看待数据。 我们的眼睛看不到(取决于您的显示器)左侧脸部的细节。 但是右边的“增强”版本在阴影中向我们显示了很多细节，使我们可以更好地了解他的脸。

So the FBI most likely doesn’t have magic Photoshop powers, and you can’t take pictures of the little green men living on Pluto with your funsaver. Don’t believe everything you see on TV!

因此，FBI最有可能没有魔术般的Photoshop功能，并且您无法使用Funsaver拍摄生活在冥王星上的绿色小人物的照片。 不要相信您在电视上看到的一切！

Image Credits: Harrison Ford from Firewall used without permission, assumed fair use. Light Writing by BloomsEyeView, Creative Commons. Garbage by Editor B, Creative Commons. IMG1189b by HooverStreetStudios, Creative Commons.

图片来源：防火墙的Harrison Ford未经允许使用，假定为合理使用。 BloomsEyeView的 轻写 ，创用CC。 编辑B的 垃圾 ，创用CC。 IMG1189b，作者： HooverStreetStudios ，知识共享。

翻译自: https://www.howtogeek.com/howto/43078/stop-believing-tvs-lies-the-real-truth-about-enhancing-images/

飞机安全谎言