[yosito]

I'd love to see some examples of these reconstructed images. Especially ones that come from photos that were taken before this technology was developed. This work reminds me of a technology I read about a few years ago that could determine an actor's heart rate from movie footage. There's so much hidden information in the recordings that we make, if we can figure out how to extract it.

[jakobegger]

There isn't enough data in normal photographs.

The techniques presented here only work if you set up the environment, or at least know a lot about it. The only case where they could actually reconstruct an object, was an extremely elaborate setup with a very simple scene, and a controlled laser to scan the scene.

Just letting your algorithm run over random photos won't reveal a thing.

[Cogito]

> The techniques presented here only work if you set up the environment, or at least know a lot about it.

Sorry which techniques are you referring to? It's probably this one, but this is just one small part of the article

While Freeman, Torralba and their protégés uncover images that have been there all along, elsewhere on the MIT campus, Ramesh Raskar, a TED-talking computer vision scientist who explicitly aims to “change the world,” takes an approach called “active imaging”: He uses expensive, specialized camera-laser systems to create high-resolution images of what’s around corners.

The article starts by describing an observation of very poor quality camera obscuras (the faint image of the outside world you sometimes get through a window) and goes on to talk about many other ways to get information out of images that isn't easily seen. Most of those techniques seem to use two images of the same scene, with some change, to actually get access to that information. That means most old photos cannot be analysed, but old videos can be.

Imagine you’re filming the interior wall of a room through a crack in the window shade. You can’t see much. Suddenly, a person’s arm pops into your field of view. Comparing the intensity of light on the wall when the arm is and isn’t present reveals information about the scene. A set of light rays that strikes the wall in the first video frame is briefly blocked by the arm in the next. By subtracting the data in the second image from that of the first, Freeman said, “you can pull out what was blocked by the arm” — a set of light rays that represents an image of part of the room. “If you allow yourself to look at things that block light, as well as things that let in light,” he said, “then you can expand the repertoire of places where you can find these pinhole-like images.”