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

Steered Mixture of Experts Regression for Image Denoising with Multi-Model-Inference