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Abstract We introduce parametric level-set enhanced to improve reconstruction (PaLEnTIR), a significantly enhanced parametric level-set (PaLS) method addressing the restoration and reconstruction of piecewise constant objects. Our key contribution involves a unique PaLS formulation utilizing a single level-set function to restore scenes containing multi-contrast piecewise constant objects without requiring knowledge of the number of objects or their contrasts. Unlike standard PaLS methods employing radial basis functions (RBFs), our model integrates anisotropic basis functions (ABFs), thereby expanding its capacity to represent a wider class of shapes. Furthermore, PaLEnTIR streamlines the model by reducing redundancy and indeterminacy in the parameterization, resulting in improved numerical performance. We compare PaLEnTIR’s performance to state-of-the art alternatives via a diverse collection of experiments encompassing denoising, deconvolution, sparse and limited angle of view x-ray computed tomography (2D and 3D), and nonlinear diffuse optical tomography tasks using both real and simulated data sets.