Image restoration based on constrained total least squares

In a constrained total least squares algorithm (CTLS), the selection of a minimal algebraic set of linearly independent random variables to express the noise matrix ΔC is an important task. A fast algorithm is provided using the possibly dependent random variables set. We showed that it can be viewed as a combination of the CTLS method of V.Z. Mesarovic et al. (see IEEE Trans. Image Process., vol.4, p.1096-107, 1995) and the RLS method when the noise is Gaussian. Our experimental study indicates that our algorithm has better visual and objective quality, while having a much lower computation cost. Moreover, our algorithm can also handle a more general noise model.