  Total Variation (TV) based regularization has been widely applied in
restoration problems due to its simple derivative filters based formulation and
robust performance. While first order TV suffers from staircase effect, second
order TV promotes piece-wise linear reconstructions. Generalized Multi-Order
Total Variation (GMO-TV) is proposed as a novel regularization method which
incorporates a new multivariate Laplacian prior on signal derivatives in a
non-quadratic regularization functional, that utilizes subtle
inter-relationship between multiple order derivatives. We also propose a
computational framework to automatically determine the weight parameters
associated with these derivative orders, rather than treating them as user
parameters. Using simulation results on ECG and EEG signals, we show that
GMO-TV performs better than related regularization functionals.
