Unfortunately there is a loss of information for physical images i.e. images with finite signal to noise ratio per pixel. An out of focus lens acts like a linear transformation, i.e. a matrix between the focused ideal image and the actual image. To reverse that transformation we have to calculate the inverse of the matrix. Depending on the severity of the blurring that inverse may not exist or it may, if it exists, amplify noise and sampling errors in the resulting image very strongly. Imagine the worst case blurring matrix of a two pixel image:
This matrix is singular and can not be inverted at all.
Take a less severe case (20% blurring), now the matrix is
and the inverse of that is:
There are two problems with this one: because of negative coefficients in the inverse you may end up with negative pixel values in the reconstructed image, which is unphysical. Secondly the diagonal elements are larger than one, which amplifies noise.
Having said that, one can achieve remarkable results if the resulting image has a very high signal to noise ratio and if the inverse transformation can be reconstructed with high precision.
If you are interested in this area I would urge you to do your own experiments with a few matrices to get a feel for what's going on. Ideally image blurring is a local phenomenon, i.e. we can restrict ourselves to areas of an image that are only a few (maybe 2-5) pixels wide. This reduces the problem to small matrices. Wolfram Alpha can do the matrix inversion for you, so you don't have to set up any math package (although numpy is easy to use, if you know Python).
As for the experimental side of it, the proper way to calibrate a lens requires to produce a series of high contrast test images of either pinholes (delta functions) to retrieve the blurring matrix directly or, even better, to use high frequency stripe patterns to measure the blurring in the Fourier domain.