# Where Does AI Get the Missing Detail in a Low-Resolution Image? > Source: > Published: 2026-08-17 16:01:02+00:00 You’ve seen it in a hundred crime shows: “zoom in… enhance,” and a smear of pixels snaps into a crisp face. For decades, that was pure fantasy. Now AI can actually do it, sort of. But that raises a genuinely strange question: if the detail was never captured, where does it come from? The honest answer is stranger than the magic. The AI isn’t recovering the detail. It’s inventing it. Picture a detective leaning over a grainy security-camera still. A blurry blob of a face. “Can you clean that up?” The technician taps a key, the image sharpens, and suddenly there’s a suspect. Every viewer knows this scene, and every engineer used to roll their eyes at it, because it broke a basic law of information: you cannot recover detail that was never recorded. The pixels simply aren’t there. And yet, walk into any phone store today and the demos will show you exactly this. Blurry moon, tap, crisp craters. Pixelated old photo, tap, sharp faces. Modern AI “super-resolution” really does turn low-res into high-res, and the results can be jaw-dropping. So did the movies turn out to be right after all? Not quite. Something much more interesting is going on, and once you understand it, you’ll never look at an “enhanced” image the same way. Because the detail in that sharpened photo is real-looking, but it isn’t real. The AI didn’t find it hiding in the pixels. It made an educated guess, a plausible invention, learned from millions of other images: about what might have been there. This article is about where that detail actually comes from, how the trick works in plain terms, and the real-world moments when the guessing went spectacularly, revealingly wrong. Let’s start with why the honest answer has to be “the AI made it up.” Here’s the thing the crime shows get wrong at the most basic level. When an image is low-resolution, the missing detail isn’t hidden, squished into the pixels, waiting to be extracted. It’s gone. Thrown away. Permanently. Think about what “low resolution” actually means. A high-res photo of a face might use 200 pixels across the eye. Shrink it down, and those 200 pixels get averaged together into, say, 10. That averaging is a one-way street: once you’ve blended 20 different shades into a single gray block, there is no formula in the universe that can tell you what the original 20 shades were. Many different originals average to the same gray. The information didn’t move; it was destroyed. I wanted to prove this to myself rather than just assert it, so I ran a tiny experiment, and the result is the whole point of this article in one picture. Look closely. On the left is one blurry input. On the right are five completely different sharp images: one smooth, one striped, one checkered, one swirled, one noisy. Here’s the key: every one of those five images, when you shrink it back down, produces the exact same blurry input on the left. I engineered them that way on purpose, and verified it; the difference between each one’s shrunk-down version and the target was essentially zero. Sit with what that means. If five different sharp images all collapse to the same blur, then when you’re handed only the blur, there is no way to know which of the five was the real one. Not for a human, not for AI, not for any algorithm that could ever exist. The blur is genuinely compatible with all of them, and with billions of others. Going backwards isn’t hard. It’s impossible to do correctly, because there is no single correct answer. So when AI hands you one sharp image, it hasn’t solved an impossible problem. It has simply picked one of the countless possibilities, and the whole game is in how it chooses. Before AI, upscaling software was at least honest about its ignorance. The classic method, called interpolation (you may have seen “bicubic” in an image editor), works by a simple, humble rule: to fill the gap between two known pixels, just blend them smoothly. A dark pixel next to a light one? Put a medium-gray one between them. It’s basically educated averaging. The result of interpolation is an image that’s bigger but blurry, soft, smeared, obviously “zoomed in.” And that blur is actually a form of honesty. The software is telling you, in effect, “I don’t know what’s between these pixels, so here’s a smooth guess that doesn’t pretend to know.” It never invents a sharp eyelash or a crisp crater, because it has no idea whether one was there. AI super-resolution does something completely different. Instead of politely blending, it draws on everything it has ever seen and asks a bolder question: “Given how the real world usually looks, what sharp image most plausibly would have produced this blur?” And then it draws that image in sharp eyelashes, crisp craters, and all. This is where the missing detail comes from. Not from your photo. From the AI’s vast memory of other photos. The technical name for this memory is a prior, the model’s built-in sense of what images normally look like, soaked up from the millions (often billions) of pictures it trained on. When the AI sees a fuzzy oval with two dark smudges, its prior says “that’s almost always a face, and faces have sharp eyes right about there,” so it confidently paints in sharp eyes. It’s not recalling your eyes. It’s drawing a plausible pair of eyes, based on all the eyes it has ever studied. The detail is a statistical best-guess, rendered so crisply that it looks like a fact. There are two main ways modern AI conjures this plausible detail, and you don’t need any math to understand either one. **Method 1: the forger versus the detective.** This is the famous “GAN” (generative adversarial network), and it works like a duel between two AIs. One, the generator, is a forger: its job is to take the blurry image and invent a sharp version. The other, the discriminator, is a detective: its job is to look at an image and decide whether it’s a real photo or one of the forger’s fakes. You train them against each other, over and over, millions of times. Every time the detective catches a fake, the forger learns to do better. Eventually the forger becomes so good that the detective can’t tell its inventions from real photographs. At that point you have an AI that produces sharp images so convincing they look completely real, even though every detail was fabricated to fool a critic. That “fooling” is exactly why the output looks so authentic, and exactly why you shouldn’t trust it as truth. **Method 2: sculpting a picture out of static.** This is the newer “diffusion” approach (the same family of AI behind image generators like those that make art from text prompts). It sounds bizarre but is beautifully simple: start with a screen of pure random noise, TV static, and then repeatedly clean it up, step by step, each time nudging the static a little closer to “what a real image looks like,” while keeping it consistent with your blurry input. Do this dozens of times and a crisp image gradually emerges from the noise, like a sculptor chipping a figure out of marble. The detail wasn’t hidden in the static any more than a statue is hidden in a block of stone. It was grown, guided by the AI’s sense of realism and loosely anchored to your blurry photo. Different machinery, same fundamental move: neither one finds the missing detail. Both generate a plausible version of it. Which is a wonderful magic trick, until you remember that a plausible guess and the truth are not the same thing. And sometimes the gap between them becomes a scandal. In March 2023, a Reddit user named ibreakphotos ran an experiment so clean it became instantly famous, and it exposed exactly what “AI detail” really is. Samsung had been advertising its Galaxy phones’ “Space Zoom” as capable of taking stunningly detailed photos of the Moon, crisp craters, visible seas. Skeptical, iBreak Photos set a trap. They took a real photo of the Moon, deliberately blurred it into a soft, detail-free smudge, displayed that blurry blob on a computer monitor, turned off the lights, walked to the far side of the room, and photographed the monitor with a Samsung phone. If the phone were honest, it could only capture a blurry photo of a blurry blob. There was no lunar detail left to capture; they’d destroyed it themselves. The photo the phone produced showed a crisp, detailed Moon, complete with craters that were not in the blurred image at all. “It’s not adding detail from multiple frames… all the frames contain the same amount of detail. None of the frames have the craters, because they’re intentionally blurred, yet the camera somehow miraculously knows they’re there.” — u/ibreakphotos Then came the detail that settled the argument. In a follow-up, someone added a small gray square onto the blurred Moon, a blemish that exists on no real Moon. The phone dutifully “enhanced” it into a realistic-looking crater. The AI wasn’t revealing what was there; it was painting in what it expected to be there, based on the hundreds of Moon photos it had been trained on. Samsung, for its part, responded that its “Scene Optimizer” uses a “detail enhancement engine” and “does not apply any image overlaying”, a careful phrasing that sidesteps the deeper point: the crisp craters came from the AI’s memory of the Moon, not from the light that entered the lens. This is the whole thesis made concrete. The Moon is a perfect victim for this trick precisely because it always looks the same, so an AI can memorize it and confidently repaint it every time. The detail looks real because it is real Moon detail. It’s just not your Moon detail. It was borrowed from other photos and pasted onto your blur. The Moon case is funny. This next one is not, and it shows why “the AI invents the detail” can be genuinely harmful. In June 2020, researchers released a face-upscaling tool built on an algorithm called PULSE (from Duke University). Give it a blurry, pixelated face, and it would produce a sharp, photorealistic one. Then someone fed it a low-resolution photo of Barack Obama, recognizable to any human, and the tool “enhanced” it into a sharp face of a white man. People quickly found the same thing happened with pixelated photos of other people of color, including Alexandria Ocasio-Cortez and Lucy Liu: the outputs skewed white. The tool wasn’t malfunctioning. It was doing exactly what we’ve been describing, and doing it honestly. PULSE didn’t restore Obama’s face; it can’t; the detail is gone. Instead, it searched through the space of realistic faces it could generate (using a model trained mostly on a dataset of largely white faces from Flickr) to find one that, when shrunk back down, matched the blurry input. Since its “prior”, its sense of what faces look like, leaned heavily white, its plausible guesses leaned heavily white too. “There is a lot of information about a real photo in one pixel of a low-quality image, but it cannot be restored. This neural network is only trying to guess how a person should look.” — Alexander Malimonov, on the face-upscaling tool The incident set off a fierce public debate (including a famous argument between AI researchers Yann LeCun and Timnit Gebru) about where the bias comes from: the data, the method, or the framing. But for our question, the lesson is razor-sharp: when an AI invents missing detail, it invents it in its own image, shaped by whatever it was trained on. The guess isn’t neutral. It carries the biases of its training data, and it renders them as confident, photorealistic “fact.” A tool that turns a Black man white while claiming to “enhance” his photo isn’t just wrong; it’s wrong in a way that looks authoritative. Put the Moon and the face side by side and the real danger comes into focus, and it’s not about phones or party tricks. It’s about trust. The whole point of a photograph is that it’s a record of what was actually there. When AI invents detail, it produces something that looks exactly like that kind of record- crisp, specific, authoritative- but is really a hallucination: a plausible guess dressed up as a fact. And humans are wired to trust sharp images. A blurry photo says “I’m uncertain.” A crisp one says “this is what happened.” AI super-resolution takes uncertainty and paints confidence over it. That’s the hazard. The stakes get real fast. Imagine “enhancing” a blurry surveillance image to identify a suspect. The AI will happily produce a sharp, specific face, but that face is its guess at a plausible face consistent with the blur, not the actual person. It could invent a face that belongs to no one, or, as PULSE showed, one skewed by biased training data. That’s why serious forensic experts warn against using AI upscaling as evidence: it manufactures detail that looks like proof but isn’t. The same caution applies to “enhanced” medical images, satellite photos, or any picture where someone might make a decision based on detail the camera never actually captured. None of this means the technology is bad. AI super-resolution is genuinely wonderful for the things it’s meant for: making an old family photo look nicer, sharpening a video game, upscaling a movie for a 4K screen, or guessing helpfully where a guess is all anyone needs. The trouble only starts when we forget that we’re looking at a guess, when we treat the invented detail as recovered truth. So, where does AI get the missing detail in a low-resolution image? Now you know the honest answer: it doesn’t get it from the image at all. The detail was destroyed when the image was shrunk, and no amount of cleverness can bring back information that no longer exists. What the AI does instead is invent, it draws on a vast memory of millions of other images to paint in the most plausible detail it can, then renders that guess so crisply that it looks like a fact. Sometimes that guess is close enough to be delightful and useful. Sometimes, as with the fake Moon, it’s borrowing detail from other photos and quietly passing it off as yours. And sometimes, as with the white Obama, it reveals that the AI’s “plausible” is warped by the narrow slice of the world it learned from. In every case, the crisp result is not a window onto what was really there. It’s the AI’s best imitation of what might have been there, a beautiful, confident, occasionally dangerous fiction. The old crime-show command was “zoom in and enhance.” The accurate version would be “zoom in and imagine.” Because that sharp new detail isn’t hidden in your pixels waiting to be found. It’s being dreamed up, right now, by a machine that has seen a great many images and is making its best guess about yours. Wonderful, useful, and worth remembering every single time an image gets miraculously sharper than it has any right to be. [Where Does AI Get the Missing Detail in a Low-Resolution Image?](https://pub.towardsai.net/where-does-ai-get-the-missing-detail-in-a-low-resolution-image-dc70d4c2a164) was originally published in [Towards AI](https://pub.towardsai.net) on Medium, where people are continuing the conversation by highlighting and responding to this story.