Denoising of Digital Radiographic Images with Automatic Regularization Based on Total Variation

We report here a principled method for setting the regularization parameter in total variation filtering, that is based on the analysis of the distribution of the gray levels on the noisy image. We also report the results of an experimental investigation of the application of this framework to very low photon count digital radiography that shows the effectiveness of the method in denoising such images. Total variation regularization leads to a non-linear optimization problem that is solved here with a new generation adaptive first order method. Results suggest a further investigation of both the convergence criteria and/or the scheduling of the optimization parameters of this method.