Zoom and Enhance

crazy zoom and enhanced thing. You guys, You guys are familiar with the TV, right, I need some help you guys, what why is that there's been a theft? What was stolen? Well, somebody broke into my home and they stole all of my VHS tapes of the Super Mario Show. Wow, that's all of them. That's not necessarily a loss, but I appreciate that you're hurting Lauren. No, this is a loss not just for Joe over all mankind. It was the Super Mario Brothers Super Show and now I don't have it,

but I've got a lead. Okay, So I had a digital security camera installed in my home, not too, as long as you have a VHS collection of Super Mario. But you have a digital security camera digital security camera, and it it records video in still frames of Oh, I guess they're about a hundred pixels each, so very low resolution. That's a hundred pixels, I mean. Well, anyway, so I got together puzzles that had a hundred pieces, so it's not that much. And it takes a picture

every sixty seconds, so that's not bad. I don't call that video. I call that a series of very crappy still photos. But I have a photo of the thief, okay, all right, and I'm going to try to find him. It could also be sasquatch. We don't know you, but I need your help, okay, to identify this person. Right. So do we I know here that we got a lot of techie people here in the office. Do we have one of those enhanced computers? I can just scan this in, right, and and we can press enhance and

it will give us the dudes face. No, we don't have one of those. Why don't we have They've got to be pretty cheap by now, right, No, So alright, so what what we're referring to? Here's this trope. I first of all, Joe, I certainly hope that that was just an example story that you haven't actually suffered a terrible loss of that. Just kidding, all right, good, So so the Super Mario videotapes are safe. Okay, good. So

what we're talking about is a television trope. In fact, one of my favorite websites to just waste time on is one of the TV tropes sites where you can just read about all these these sort of cliches that have been used in television and film for about as long as the those types of media have existed. Really um, and some of them are a little newer than others. Zoom and enhanced, pretty new because we didn't have those kind of we didn't even think about those kind of

capabilities until maybe the last couple of decades. Okay, so it may have been earlier than this. But the earliest example I can think of is in N two the movie Blade Runner Ridley Scott Harrison Ford um based on the Philip K. Dick. But there's a scene in it where Harrison Ford's character is doing some investigating. Yeah, he's got a photograph and he's got it on his computer screen and he keeps shuffling around, looking at different frames of the photograph and then zooming way in and enhancing

the photo. Yeah, it's um, it's it's the way TV tropes defines it. They call it the enhanced button, very similar to the scenario you were talking about the very top of the show, Joe, He says, a staple of any crime drama. The enhanced button on the computer is able to turn a tiny, blurred, grainy image in a photo or video into a clear, unmistakable piece of evidence.

This process is virtually instantaneous, unless added dramatic tension is required, in which case extra technobabble or more applied flabottanum may be needed. These, by the way, or other tropes may require someone to stand next to the computer intoning enhance, enhance for full effect. So yeah, this idea of taking something like an image a photograph that is imperfect, it has a limited amount of data in it, and then enhancing it so that it becomes something useful, something recognizable,

is that a thing? Is that possible? All right? At least the way it's done in Hollywood? Not so much. Well, it's not possible the way they do it because in these movies and TV shows. You're just getting information that just blatantly just was not there before, right, Right, you can only enhance up to as much information as actually exists in the physical record of that infra exactly. You

can't extrapolate new things whole cloth. Right The way, the way I'd put it is that there's sort of a bottleneck on data at the moment an image is captured. Um data comes into the camera and you record a certain amount of it, and then data comes out later. Now, you can manipulate that later data in all kinds of ways, but you can't ever put in more than came in through the lens to begin with, well, not not directly onto the raw image file. You can manipulate it enough

and through guesswork. Really you're you're kind of you're kind of making some assumptions where you can fill in information that is missing. But it's not like you are uncovering the information that was there. You're actually creating new stuff to go in with the stuff that was captured at that moment. Right. It would be like if you saw,

for example, somebody's shadow in a photograph. Right now, if somebody was a really good I don't know, this is a real thing, but it was really good at identifying humans. But the shadows they cast, they can do the same kind of thing. They can look at the shadow and the picture and say, oh, it's probably this person, But you still wouldn't actually have that person's image somewhere hidden

in the file right. And you know, also, like if you've got a picture of the back of a person's head, it's not like the image of their face is contained in that data somewhere, and you can't just just rotate person and and have you know, there's no button to do that. It's amazing how often you can do that in science fiction though, like turn it around. I want to see this. I want to see this still image from the opposite angle, as if we can magically place

the camera anywhere after the effect. Uh, what what you can do in a virtual environment? I mean, there are camera tricks you can do in a virtual environment that completely defy all laws of physics because that they aren't a problem in a virtual environment. So, for example, in a video game, there are a lot of video games where you can capture the footage of you playing while you're playing the video game, and then watch it later and you can even watch it from various camera angle

is depending upon the type of game. Like, some games give you essentially free reign. You can place the camera anywhere you like. You can have a free roaming camera and move it dynamically as the scene plays back. So even though while you were playing you had one set of perceptions, you know, you might have been able to move the camera around then too, but you were limited at that time by whatever was happening right then and there. But in playback you may be given unlimited freedom. Now

that's just not the case with real life. I mean, obviously you know it's that that that data exists in real life, but if there's nothing there to record it right then, So when I was talking about the you know, creating stuff so that you can fill in the gaps, it's where you're you're not again, you're not uncovering lost information. You're not or or obvious skated information. That's the way it comes across in these television and film examples where the answer is hidden in the file and you just

have to dig it out. Yeah. Yeah, there's just a button on your computer that somehow makes it go from blurry to not blurry, and that it was the info was always there, it just needed to be not blurry. That's not the case. That's not the way it works. Um. There was a great article and Wired that talked about this and talked about some approaches that that various technicians have made to really uh address this issue. You know, I was talking about creating data to fill in those gaps,

the stuff that's not actually there in these photos. You know, in the Hollywood and TV versions, it always seems like the information is there, it's just blurry or whatever. And when you hit this button, it removes whatever that problem was and you get to look at the information that was always there. That's not the case, right. We're talking about photos and video where stuff is missing. But you can start to fill in some of those gaps by

creating stuff, by making guesses. And there was a great article and Wired that talked about this and talked about compressed sensing and sparsity. These are concepts that are used by technicians to kind of fill in information that might be missing, either a file. You know, maybe maybe the image is just really fuzzy. It could be an old photograph, or it could just have been made with a poor camera, or maybe something was on the lens or whatever, and

we can use these techniques to try and fill in information. Now, in the Wired article, they had a really interesting analogy. They said that, imagine that you have a book where on one page of the book you have almost half or maybe even more of the information missing. So you've got just missing words in sentences. Now on the pages before, in the pages after, you've got some information there, But on the page that you're interested in your missing words.

It would be like trying to extrapolate what those missing words were just based on the little bits of information you had on the previous and following pages. It's really difficult to do. However, with compressed sensing, there was an interesting development. There was a guy named Emmanuel Condez who

was looking at what an image called the shep logan phantom. Now, this is actually an image that technicians you us in order to test imaging algorithms, and it kind of looks like a an alien with a slightly raised eyebrow, kind of a snarky alien. Now, what what he did was he took an older, fuzzy version of the image, not an older one, but a fuzzy one. So he was testing an algorithm, trying to see if a particular technique would allow him to uh to sharpen this image up

a little bit. And the way the technique works is it looks for the simplest approach to filling out the information that's missing by look it's samples pixels in the image, and then from those samples, it starts to create simple shapes that are their color matched to the various parts of that sampled image. Now it tries to use the fewest number of these shapes to fill out this this photo or this this picture doesn't have to just be

a picture. By the way, you can actually apply the same technique to other kinds of media, including music, where you know you might have a low sample rate for an old MP three and you want to try and enhance it. It could do the same sort of thing with that right right, Basically, anything with a with a wave form is going to operate under very similar principles. So what they're doing here is the reason why he was doing this in the first place was not just

to get a sharper picture of an alien. The idea was to try and enhance m r I images because the best way to get a very very clear mr I image is to put someone in an MRI machine and have them stay perfectly still for a couple of minutes. But to stay perfectly still for a couple of minutes usually means having to put them under anesthesia. So they actually stopped breathing. That's how still they needed people to be. Now,

that's not really that great an option. So he was looking at what if I took this approach to try and take an image that was captured in say forty seconds as opposed to two minutes, and then try to use this this uh, this simple technique to see if I can sharpen it up. So he runs the the image through this algorithm he's created and turned out that the resulting image was a perfect match two or a near perfect match to the UH the original version of

the shep Logan phantom image. And he thought, well, that's weird. That can't be right. There's no way that worked. And so he tested it again and it got the same result and ended up showing it off to some other folks and and they really began to put their heads together and wrote a white paper on it and a research paper all about this UH technique, and um, yeah, it takes about a hundred thousand pixels for example, and just really focus on those and build out these shapes

and it could build a usable image. But um, a couple of caveats. One is that it can take a few hours to do this as the algorithm goes through all the different variations of the simplest way of approaching this, And there is a chance that the resulting image that you get back at the end is not a match for what it should have been. There is that chance.

It's a small chance according to the researchers, but it can happen because the computer is just basically guessing based upon all the little bit of information that it has. So in that sense, if you have this this area of doubt where you know, you might say, well, you know, according to our computer model, this is what the image would have looked like if we had looked at it at this scale. Uh, you you know, you have to

keep that in mind. You have to remember, like, this is what it probably looks like, not this is definitely the image. So that's another difference from the Hollywood version. Right with the Hollywood one, as soon as they do the enhance, there's our guy, there's no way it's there's our guy. It's the guy who was built third in the film, so now we know we got him, whereas this is just some dude. Yeah, So I mean that's

an interesting approach. And the whole idea of sparsity is this idea of going with the simplest um and fewest number of simple shapes. So it might be like it it detects a couple of blue pixels, uh in a in a or a few blue pixels in an area, and then it just fills out the rest of that area with the same color blue, so that it's approaching at you know, saying well this, this is probably part of a border for this thing. I'm just going to fill in the rest. And it does that thousands and

thousands of times for the entire image. But yeah, it's still not the kind of instantaneous approach we see in popular films and television so well, on a much smaller scale and in that kind of instantaneous sort of time frame. That's that's a function of photoshop. I mean, you know you can you can click that I forget what the function is called in there. You can just click that little button and have it kind of fill out what a line would have looked like, yeah, yeah, there are

too what's around it. There are a lot of algorithms out there that take this approach where it looks at the existing data and tries to extrapolate what the rest of it should be. Uh, and it's you know there and I'm talking about a few pixels, right, and they have different degrees of of uh of sophistication and and resolution and uh, but nothing on the scale of the

Hollywood version. Sure, sure, I I do think that that what we see in those kind of everyday applications is is what leads to some of the confusion about what like the professionals can do. Um like a like like with Google Maps. You know, you can you can zoom and enhance in a Google map, but that's because it's built of these multiple tiles of images then, right, that

that have greater acuity on the lower levels. It's not like when you are at you know, at like satellite view of the Earth that has all the same detail as it would if you are a low flying plane. They have different sets of images that are geolocated at particular points on the Earth, and that's you're you're shifting from one set of just to another and the real genius of the program is how it allows you to

to do that shift. Yeah, and it does in such a way where it kind of makes you feel like you are having a seamless experience, but in reality you are switching from one set of photos to another. There's a similar thing in a way, uh an idea called gigapan or gigapixel images. Gigapan is just one of the

many terms for it. So this is the idea of taking several pictures high high, high resolution pictures of a scene and then stitching all those pictures together to kind of make sort of a panoramic image, but panoramic in beyond just you know, it's a very wide photo. It could be very tall. It could and and the cool thing is that allows you to zoom in at crazy levels because instead of it being just one big picture with lots and lots of information, it's actually a collection

of mosaic of all these high resolution images. So I've seen some of like sporting events, like like the Olympics or something like that, and it's a picture of the crowd and when you first look at it, you just

see a mass of faces. It's just a huge number of people, maybe a hundred thousand people, and then you could arbitrarily say, all right, don't want to zoom in on this one section of the crowd, and you zoom in until just that one section fills up your screen, and now you can suddenly see actual details, and then you say, they kind of want to zoom in on that collection of of of folks like that that small group of people right there, and zoom in even further,

And depending upon how many photos they've taken and how high resolution the photos were, you might get to a point where you can read the text on a person's shirt, or at least be able to see what kind of basic design is on a person's clothing if they are wearing something that has a big logo on it or something, you might be able to tell um and it. The illusion is that you've got this one picture that you can just zoom in indefinitely, just like you could in

the movies. But the way you produce that file is actually by taking all these different pictures. It's not like it is a single element the way you would think from a film or TV show. There's actually sort of a whole family of image processing techniques that are known as super resolution that's the idea of taking a picture and trying to somehow increase resolution after you've already got

the final product. Um. One of the techniques that I think is interesting is, Uh, so we've talked about single frame increases in resolution, but what about multiple frames. So imagine you've got video and it's not my one image per second security camera, uh second per minute whatever I said. Um, it's it's like continuous video. You can actually put together frames inaggregate to make each frame sharper. Interesting. Actually, that's the way that the human eye works. That is basically

how we are all seeing things all the time. We see in I mean, I guess you could call it still frames, but but basically in video, and uh, and our brains kind of compile the images as I'm looking back and forth between the two of you, or um or or kind of going like what's that weird thing on the corner behind old head? No, but um, now you know, and and and your brain puts together this

information into more or less a single image. So so if you were to take a camera, let's say you've got a digital camera that could take burst photos like a whole bunch in just a blink of an eye that I assume you could apply the same sort of approach to try and create the best possible version of the picture you were trying to take. Oh, I'm sure. I mean what's operating here is that when you have multiple frames, each frame is probably giving you some type

of information that wasn't available in the frame before. So if somebody's turning their head or something like that, at different points, you see different parts of it illuminated, um some parts are closer to the perfect ideal focus, and and so by sort of selecting the best part of each of those images and and averaging them right, you can get a sharper image than you had in any

of the original frame. This is kind of like that that those commercials you see of the cameras where you can swap out people so that you just see the best, uh best faces for everyone. Like you've got the group photo, and you took a series of group photos, and you're like, well, little Billy was being a complete snot in the first for six of these, but in the seventh one he's

looking he's looking, you know, at the camera and smiling. Unfortunately, Dad has his eyes half closed because he's just about the sneeze. So we need to combine all these photos into the ideal family photo that never existed, but seems to like that moment never existed, right, the moment where everyone in the family is smiling and content and behaving never existed, but you've created the illusion that it has

by combining all these images into one. Or if you're George Lucas and you really like an actor's face in one take, but their body movements and another, so you just paste the two together in episodes one through three. Yeah, or you know, if you just don't like actors and tell them not to act. Sorry, that's a little editorializing there. This this also reminds me of the the app I

talked to you about Lauren A Group GROUPI. Yeah, this is this is one um that was created by um I Do Use Labs, which is out in uh Pakistan. But it's um It's it's an app that let's everyone be in in a photograph without any one photographer having to having to step out or having to give your you know, very expensive device to a random strange may may not make off with. So let's say let's say like we get the whole House Stuff Works crew to

go someplace. You know, we're all going to six Flags for a day, and we want to get our our picture taken in front of the great American screen machine as we are wont to do. And uh, and there's the whole group. But who takes the photo? Do we and trust our expensive house stuff works camera to some ragamuffin walking by or bugs bunny? Yeah, he can't be trusted. He can't doesn't even have opposable thumbs. So yeah, we we end up saying, well, what can we do? What

if we use group pick? Then essentially, from what I understand, what allows you to do is take at least two photos where you swap out photographers, and then you can combine the two so that you have both photographers in the full group photo. All Right, the app app helps you frame a picture and then um you uh, you know, one person takes the first picture, a second person takes

the second one. Um, you mark out who the two photographers were on screen, and uh, based on the fact that it's already framed it for you and so they're more or less identical photos. Otherwise, um, it will it will swap out the little slivers well, and you know it's it helps if you have one each each photographer on the extreme ends of the frame. Right, this would have been so useful to like, uh, despotic Soviet rulers, you know, like Joseph Stalin, But you went straight to

despotic Soviet rulers. Yeah, you know. So, so you've eliminated some political rival and you want to erase his image from pictures of you. Um now you cannot just erase him, but you can also insert your new cronies and sycophants. Right. I said that what I want to do is use this kind of thing to take a picture where there's like twenty Jonathan's all in the same photo. You can you can certainly do that your dream world, you know, but my dream, your nightmare, this all this all weirds

me out, honestly. I I mean, it's the technology is fascinating and wonderful. Um and and this kind of automatically revisionist history is I'm not sure that whether or not I need access to this technology for for a nap. It's it actually does have some somewhat troubling implication. The idea that you can manipulate images to such an extent as to create a new history that never really existed. It's kind of you know, I mean, that's a plot point and a lot of movies and television as well.

It's just now we're getting to a point where the average consumer could theoretically do that with very little training, and and we all do this all the time. I mean everything that we're seeing again, like the human eye is flawed. It's only taking in so much information and it's filling in a lot of gaps in between those frames that it's taking in. But um, but yeah, just doing that on purpose. I'm like, Okay, well, I thought i'd talk a little bit about some other kind of

cool camera tricks. There was one in particular one to talk about, um, which was this idea of being able to take photos and then change the focal point after you've taken the photo. Yeah. The light field cameras. Yeah, lightfield cameras also known as plan optic cameras, although they're not true plan optic cameras. A plan optic camera, well, it comes from the word plan us, which actually means full or complete, and then optic, of course, is the

behavior of light. A true plan optic camera is impossible. It's just a theory. It's kind of a thought experiment, because the reason why it's impossible is that it's the idea that you would be able to take, uh, all the visual information within an environment, kind of like in those virtual environments, and be able to reproduce a still

image from any angle, from any focal point. It's not really possible, not only because we can't just place a camera anywhere in the room, but also because the camera itself is going to reflect light off of it, and so the camera's presence. It's kind of like that whole idea, like the by observing something, you change the observed a sort of camera similar although Heisenberg's uncertainty principle would tell me that I know where the camera is, but I

don't know how quickly it's taking pictures. Um, that's just a little quantum joke. But anyway, it's not a true plan optic camera, but light field camera. What does is it tries to capture all the rays of light and every direction that they are traveling within a single frame of reference, a single image. So the camera that a lot of people have heard about is the light tro

which is this, uh, this really cool camera. If you were to just look at one, you would you would think that looks like some sort of prism or something, because it's not, you know, it's not camera shaped. It looks like you know, this this elongated uh cubic kind of thing, and it actually allows you to take photos and then change the focal point after you've taken them.

So if you've set up like a scene so that you've got you know, Joe, let's say that you are just crazy about war gaming, and you haven't you have an enormous collection of painted lead miniatures, oh that kind

of war gaming. No, not that you actually are buddying up to your you know, Russian despotic friends that you've already referred to in this episode, But no, that you play a tabletop war gaming games and you've got a huge collection of these painted lead miniatures and you've you've set them up into this neat uh row upon row, a battalion of soldiers and on this table, and you

take a photo from the end of the table. Now, normally you would have to set a focal point on your camera, right, you would have to say, all right, I want to focus on the front soldiers, so that everything in the background kind of fades away into fuzziness. As it goes further back, or you would set the focal point so that the ones in the back are

in focus and the ones in front are kind of blurry. Well, the litro camera captures the light field, all of those light rays traveling in every direction, and then creates essentially a virtual camera with a virtual lens within the software.

And so when you view your image, you can change the focal point and say, all right, I want to focus on the soldiers that are in the back, and it'll switch the focus to the soldiers in the back, or I want to focus on the ones in the front, and it will create essentially a virtual camera with a virtual lens and a virtual image sensor that it have created that particular image, and you can change as many times as you want. Uh. And it lives that way,

but only if you're viewing it on a computer. Obviously, if you were to ever print an image out, it would be stuck in whatever focal point you had chosen. That seems really cool, but I wonder how much space does one of those image files take up? A lot? Yeah, they does it take a long time to to process that? Not at all? It's like crazy fast. I mean, does it take a long time to take the original image.

Not at all. I mean, because you know the kind of depth of field the ear that you're talking about, Like, you know, what was so revolutionary about artists like Ansel Adams was that they were working with such large prints of film that they could gain a depth of focus that was huge. Well, it is. There is a limitation the LTRO camera. It's not like it's not the kind of camera you're gonna take with you to go um,

you know, like on a fast sightseeing tour. It's it's great for things that where you have composed a scene and you want to take a photo of that scene, or if you wanted to do something like you have a flower in front of you, and you're holding a flower in front of you and there's the Eiffel Tower in the background, you could take a picture like that and then you could swap the focus so that the

Eiffel towers and focus are the flowers and focus. But it's not the kind of thing you would just carry around to take a snap whenever you were walking around. It's it's not that that kind of cameras. It's not your no, no, it's not designed for that. It's not meant for that. UH. And you know I've I've played with one of these. I actually got a chance to play with one, and it's kind of cool. The viewfinder on the back is essentially the entire uh. It's not

even if you find her. It's a screen. It's a touch screen that is the entire uh interface. So you'd point the camera at something, you'd see the video image of it on the little of the screen on the back. It keeps saying viewfinder, but it's a screen. You tap it, it would take the photo, uh, and then you could look at the image on the screen on the back, and even in you could touch different parts of the

image and bring that part into focus. UH. And then once you upload the images, they would live on a website that LTRO owned, and you would be able to play with them and share them. That way, you could share it onto other platforms like Facebook or Twitter or whatever. People could view the images and and they could change the focal point too. So it's a living image in

that sense. So if I were to upload one of these images, Joe, you could go and look at and say, oh, what would this look like if this part we're in focus and you click on it and then it would switch, so it was a new zoom in and see the shadowy man in the window. That part is not built in yet, but who knows what could happen to the future. So that was one super cool kind of futuristic thing

that exists right now. And the lightro camera came out a couple of years ago, and has you know, sort of been uh more or less a curiosity, I would say among a certain like like the tech savvy group who heard about it early. Uh, kind, I've really dug it. I don't own one. I thought it was a neat product, but I didn't actually purchase one, but I didn't enjoy

playing with it. But then there's other like kind of futuristic ideas, like the idea of being able to use a camera to take a picture of something that's not even in the room, like it's in the room next door. How does that work? Well, you could have an X ray camera that would work, but it would also be very dangerous. Are radiating yourself every time you take a picture? Yeah, not to mention, not to mention your your don't do

it to yourself, you do it to other people. But if you're taking the photo you're still being exposed to X ray. Get my lead pants. We're doing photography, uh lead pants at six Flags. That's that's what I'm picking up from this. But anyway, so this is a concept that has been worked out over at M I T. Settle Children Settle, they called him lead pas. Okay, Joe, enough enough, we're keeping all of this. It's all going in. Yes, we are executive decision. It's all kept. Noel, you answer

to me. It's all kept. So um M I T and M I T researchers were working on this idea of being able to take images of stuff that wasn't directly within the field of view of the camera. The example used was that let's say you're shooting an image in a room and there's an open door that goes into another room. Now you do not have an angle of you into that other room. You can just see the open door that has opened into the other room.

You take an image with this camera, and then it starts to collect data and reconstruct what might be in that other room, giving you an image of let's say that there's a person hiding in there. You would see the picture of a figure in that other room, which

is a cool idea. How does this work. It's actually using very very short bursts of laser light to project laser light out that some of that laser light hits the doorway that's open and bounces off of it, and then will eventually hit stuff that's in the room, bounce off that back to the door, bounce off the door back to the camera. Now, the number of rays of light of this laser light, or the the amount of information that's coming back is a fraction of what was

sent out. Right, You're you're only getting a tiny little echo back of what you had just sent out in a burst. And they're using femto lasers, which means it's sending out a burst of of light that's a quadrillionth

of a second long. And uh, they actually have to use a special kind of shutter that closes after they shoot out this light because they don't want that initial bounce back to affect the information of the stuff that's in that of the room because anything that if you know, if if the laser light just hits the door and bounces right back to the camera, that's going to ruin

the image. So what does is it The shutter actually stays closed for a fraction of a second, then opens up to accept all more all incoming photons, and then the way it reconstructs the room that is out of you is it measures the amount of time it took that photon to come back to the camera. So it's almost like sonar, but with light um and it's a really cool idea. The only thing is that the reconstruction part,

again is probabilistic. It's the best guess, which means that you could get information back because it's such a small amount compared to what you sent out that there's a lot of extrapolation that has to happen in order for you to be able to take a look at what was in that other room. Yeah, i'd wonder if your image of what was in the room could be affected by I don't know, I mean not just solid objects,

but heat and atmospheric composition. I would imagine not I mean, we're talking lasers, so that's a very direct kind of it's measuring them the time periods between the photons. Then it's uh, yeah, I think I don't I mean, I honestly don't know the answer to that. It may very well be um, I think most of the time, like the the at least the example they gave of taking a picture of something that's happening in another room. You probably don't have to worry too much about that. Now.

If there's a lot of electromagnetic interference in there, that could end up playing a part. But I don't know. Maybe if electro is is trying to play Xbox in the room next door, that might be a bad thing. Extra technology combined in with them with with regular old photography, if you can call something like that regular old photography, is um is probably gonna going to lead us in

interesting places. Like you know, if all of our all of our cell phones basically have accelerometers in them these days, and if you can combine that data with the data that happened when you took a motion blurred photo, you could hypothetically correct for so yeah, so what essentially saying like, oh, well, the camera was moving right to left when this photo was taken. Uh, here is what it would have looked like had it been still at the moment that the

photo was snapped. Interesting. Yeah, the cool thing about this technology is that while we are not in the realm of zoom and enhanced the way we see it in movies and television. There's no question in my mind that we are heading in that direction now. It may very well be that the images we see when we zoom and enhance are a lot of guesswork, very sophisticated guesswork.

But I think we're going to get there to a point where we don't have to wait a certain number of ten hours to to get an idea of what this fuzzy photo might be of. Well, in a way, I would say that I do think in one interpretation, we are never going to get there because we're still never going to have information that wasn't no, no, we will always be limited to what's there and guesses about it. Right, But but our guesses are getting better, and our way

of recording is getting better. So like we previous cameras did not have this shoot a femto laser in new a you know, obscured room, right, And as computers get more powerful, we're um, you know a lot of the equations that people are working with right now are things that have been around since the nineteen hundreds or the eighteen hundreds. I'm sorry, um, you know, the the Furrier transform, which is a big one that um that is being passed around in most of the most of the apps

that you can download to reduce blur. Yeah, that was a dude who was born in seventy eight. So so you know that this this math has been around, but the way that we're using it right now it's pretty pretty incredible. Yeah. So, uh, we're not going to be we're not going to be doing any sort of Bones like technological wonders. I hear that they actually started to scale back on some of the more ridiculous technological things they would do in that show. I haven't watched it.

Watch it in any seasons? Yeah, So which one is Bones? Bones was the one with um David Boreennas as the cop and uh Emily Emily Deschanelle as a as the anthropologist and um and they and they had their there they solve crimes through science, science and quotes science fiction. They're they're pretty lady. Um computer scientist would would would be like like, oh hey, yeah, no, I just totally

developed this new computer that does this crazy things. So yeah, I'm excited to see where camera technology takes us in the future. Maybe we get to a point where every image you see you'll just have to keep in mind, I cannot trust that this moment ever actually happened. We're kind of already there, aren't we, Because I don't know. Do you believe that photos you see on the internet, Like when my friends on Facebook post their wedding photos is a picture of their new baby. I have to

comment shopped. This has been photoshop? Did you guys see this? Is? This plays into our conversation a little bit. Did you guys see the thing I posted? There was a guy who faked being at Comic Con. Yeah, Like a friend of his went to Comic Con and he was not going to Comic Con, but he decided, just for kicks that he would pretend that he was also at Comic Con. So he kept texting him going like, oh, hey, I'm over here in this meeting room, or are are you here?

Oh you just missed me. Oh no, I'm over here now. But he was. He had been before, so he knew enough about Comic Con to be able to, uh to to fake it and say, oh, I'm over at hall h or I'm over across at the over in the gas Lamp district getting food and just leading this poor

sucker along the entire time. One of the things he did was he found he scoured Twitter for images taken an instagram taken from from comic Con, found one of these two guys, like it was a couple of celebrities posing together, and so then he matched himself, uh, standing in an alley way behind his house, and then photoshopped himself into the photo, placing the guy one of the two celebrities, so it looked like he was hanging with one of these other guys, and then uploaded that and

sent it to his friends, say, I just ran into so and so here we are together, and it looked great. I mean it didn't like upon casual glance, it did not appear to be a photoshoped image. Now, if you were to look really closely, you'd say the lighting is really weird because his face has not lit exactly the same way. But you know, if you're just looking casually, you wouldn't think anything of it. So Yeah, at this point,

I think you're right, Joe. I think we have to just assume that everyone's photos of their wedding and babies and everything is chopped. Yeah, everything is well, guys. Uh, that kind of wraps up our discussion. What do you guys think about the future of photography and videography and cameras in general. Is it's something that's exciting to you. Are you a photoshop wizard? Do you have lots of examples of crazy photoshops? Do you want to do some

crazy photoshops of the hosts of Forward Thinking. It's gonna happen. I might as well ask it happens. Well, guess what. We have images of all the hosts of Forward Thinking. You can find those over at our Facebook page. They're they're up there. You can also if you hunt around, you'll you'll find photos of us. I can't wait to

see where we end up. By the way, if you want to get in touch with us, you can email us our addresses fw thinking at discovery dot com and go to fow thinking dot com for all the blogs, podcasts, videos, lots of other interesting material there. We look forward to hearing from you, and we'll talk to you again really soon. For more on this topic in the future of technology, visit forward thinking dot com. Brought to you by Toyota. Let's go places