Photothermal measurements with an infrared camera enable a fast and contactless part inspection. The main drawback of existing reconstruction methods is the degradation of the spatial resolution with increasing imaging depth, which results in blurred images for deeper lying structures. In this work, we propose an efficient image reconstruction strategy that allows prior information to be included to overcome the diffusion-based information loss. Following the virtual wave concept, in a first step we reconstruct from the measured photothermal signal an acoustic wave field that satisfies the standard wave equation. This wave is called a virtual one, because it is not the measured acoustic wave but mathematically calculated from the temperature signal measured on the sample surface. In the second step, stable and efficient reconstruction methods developed for photoacoustic tomography are used. We compensate for the loss of information in thermal measurements by incorporating the prior information positivity and sparsity. For that purpose we combine circular projections with an iterative regularization scheme. Using experimental data, this work demonstrates that the quality of the reconstruction based on photothermal measurements can be significantly enhanced. The main goal of this work was to illustrate that prior information significantly improves the regularized solution and, hence, the reconstructed field. Using an iterative non-linear regularization method, the prior information positivity and sparsity could be incorporated. The regularization and reconstruction results show that respecting information available about the data significantly increases the quality of the regularized solution.