Compressed sensing is a technique for acquiring and reconstructing a signal utilizing the prior knowledge that it is sparse or compressible.
The main idea behind compressed sensing is to exploit that there is some structure and redundancy in most interesting signals -- they are not pure noise. In particular, most signals are sparse, that is, they contain many coefficients close to or equal to zero, when represented in some domain.
The thing is that while the space of all images has 2MB worth of “degrees of freedom” or “entropy”, the space of all interesting images is much smaller, and can be stored using much less space, especially if one is willing to throw away some of the quality of the image.
What if the camera selects a completely different set of 100,000 (or 300,000) wavelets, and thus loses all the interesting information in the image?
The solution to this problem is both simple and unintuitive. It is to make 300,000 measurements which are totally unrelated to the wavelet basis - despite all that I have said above regarding how this is the best basis in which to view and compress images. In fact, the best types of measurements to make are (pseudo-)random measurements - generating, say, 300,000 random “mask” images and measuring the extent to which the actual image resembles each of the masks. Now, these measurements (or “correlations”) between the image and the masks are likely to be all very small, and very random. But - and this is the key point - each one of the 2 million possible wavelets which comprise the image will generate their own distinctive “signature” inside these random measurements, as they will correlate positively against some of the masks, negatively against others, and be uncorrelated with yet more masks.
But (with overwhelming probability) each of the 2 million signatures will be distinct; furthermore, it turns out that arbitrary linear combinations of up to a hundred thousand of these signatures will still be distinct from each other (from a linear algebra perspective, this is because two randomly chosen 100,000-dimensional subspaces of a 300,000 dimensional ambient space will be almost certainly disjoint from each other). Because of this, it is possible in principle to recover the image (or at least the 100,000 most important components of the image) from these 300,000 random measurements. In short, we are constructing a linear algebra analogue of a hash function.