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Research in the past years introduced Steered Mixture-of-Experts (SMoE) as aframework to form sparse, edge-aware models for 2D- and higher dimensionalpixel data, applicable to compression, denoising, and beyond, and capable tocompete with state-of-the-art compression methods. To circumvent thecomputationally demanding, iterative optimization method used in prior works anautoencoder design is introduced that reduces the run-time drastically whilesimultaneously improving reconstruction quality for block-based SMoEapproaches. Coupling a deep encoder network with a shallow, parameter-free SMoEdecoder enforces an efficent and explainable latent representation. Our initialwork on the autoencoder design presented a simple model, with limitedapplicability to compression and beyond. In this paper, we build on thefoundation of the first autoencoder design and improve the reconstructionquality by expanding it to models of higher complexity and different blocksizes. Furthermore, we improve the noise robustness of the autoencoder for SMoEdenoising applications. Our results reveal that the newly adapted autoencodersallow ultra-fast estimation of parameters for complex SMoE models withexcellent reconstruction quality, both for noise free input and under severenoise. This enables the SMoE image model framework for a wide range of imageprocessing applications, including compression, noise reduction, andsuper-resolution.

Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoising