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For several decades, image restoration remains an active research topic inlow-level computer vision and hence new approaches are constantly emerging.However, many recently proposed algorithms achieve state-of-the-art performanceonly at the expense of very high computation time, which clearly limits theirpractical relevance. In this work, we propose a simple but effective approachwith both high computational efficiency and high restoration quality. We extendconventional nonlinear reaction diffusion models by several parametrized linearfilters as well as several parametrized influence functions. We propose totrain the parameters of the filters and the influence functions through a lossbased approach. Experiments show that our trained nonlinear reaction diffusionmodels largely benefit from the training of the parameters and finally lead tothe best reported performance on common test datasets for image restoration.Due to their structural simplicity, our trained models are highly efficient andare also well-suited for parallel computation on GPUs.

On learning optimized reaction diffusion processes for effective image restoration