  Research in the past years introduced Steered Mixture-of-Experts (SMoE) as a
framework to form sparse, edge-aware models for 2D- and higher dimensional
pixel data, applicable to compression, denoising, and beyond, and capable to
compete with state-of-the-art compression methods. To circumvent the
computationally demanding, iterative optimization method used in prior works an
autoencoder design is introduced that reduces the run-time drastically while
simultaneously improving reconstruction quality for block-based SMoE
approaches. Coupling a deep encoder network with a shallow, parameter-free SMoE
decoder enforces an efficent and explainable latent representation. Our initial
work on the autoencoder design presented a simple model, with limited
applicability to compression and beyond. In this paper, we build on the
foundation of the first autoencoder design and improve the reconstruction
quality by expanding it to models of higher complexity and different block
sizes. Furthermore, we improve the noise robustness of the autoencoder for SMoE
denoising applications. Our results reveal that the newly adapted autoencoders
allow ultra-fast estimation of parameters for complex SMoE models with
excellent reconstruction quality, both for noise free input and under severe
noise. This enables the SMoE image model framework for a wide range of image
processing applications, including compression, noise reduction, and
super-resolution.
