Research in light field (LF) processing has heavily increased over the last decade. This is
largely driven by the desire to achieve the same level of immersion and navigational …

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 …

Steered-Mixtures-of-Experts (SMoE) models provide sparse, edge-aware representations,
applicable to many use-cases in image processing. This includes denoising, super …

The Steered Mixture-of-Experts (SMoE) framework targets a sparse space-continuous
representation for images, videos, and light fields enabling processing tasks such as …

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 …

The Steered-Mixture-of-Experts (SMoE) model is an edge-aware kernel representation that
has successfully been explored for the compression of images, video, and higher …

Kernel image regression methods have shown to provide excellent efficiency in many image
processing task, such as image and light-field compression, Gaussian Splatting, denoising …

Compression algorithms that employ Mixtures-of-Experts depart drastically from standard
hybrid block-based transform domain approaches as in JPEG and MPEG coders. In …

Steered Mixture-of-Experts (SMoE) is a novel framework for representing multidimensional
image modalities. In this paper, we propose a coding methodology for SMoE models that is …

Kernel methods have been studied extensively in recent years. We propose a three-
dimensional (3-D) Epanechnikov Mixture Regression (EMR) based on our Epanechnikov …