  In ground based infrared imaging a well-known technique to reduce the
influence of thermal and background noise is chopping and nodding, where four
different signals of the same object are recorded from which the object is
reconstructed numerically. Since noise in the data can severely affect the
reconstruction, regularization algorithms have to be implemented. In this paper
we propose to combine iterative reconstruction algorithms with robust
statistical methods. Moreover, we study the use of multiple chopped data sets
with different chopping amplitudes and the according numerical reconstruction
algorithm. Numerical simulations show robustness of the proposed methods with
respect to noisy data.
