  In optoacoustic tomography, image reconstruction is often performed with
incomplete or noisy data, leading to reconstruction errors. Significant
improvement in reconstruction accuracy may be achieved in such cases by using
nonlinear regularization schemes, such as total-variation minimization and
$L_1$-based sparsity-preserving schemes. In this paper, we introduce a new
framework for optoacoustic image reconstruction based on adaptive anisotropic
total-variation regularization, which is more capable of preserving complex
boundaries than conventional total-variation regularization. The new scheme is
demonstrated in numerical simulations on blood-vessel images \textcolor{black}
{as well as on experimental data} and is shown to be more capable than the
total-variation-$L_1$ scheme in enhancing image contrast.
