  In this paper we introduce a novel block-based regression strategy for image
denoising based on edge-aware Steered-Mixture-of-Experts (SMoE) models. SMoEs
provide very sparse image representations, able to model sharp edges as well as
smooth transitions in images efficiently with few parameters. A multi-model
inference strategy is developed that improves significantly the denoising
capacity of single SMoE models. We show that the important edge reconstruction
properties of SMoEs are well preserved, even when many models are fused under
severe noise. We investigate model-inference from local neighborhood blocks as
well as from distant blocks using block-matching as in BM3D. Our initial
results indicate that SMoE multi-model regression can provide promising results
compared to state-of-the-art BM3D with excellent edge quality.
