Universal Image Artifact Reduction via Heterogeneous Mixture of Experts

Abstract:
This master thesis proposes a novel unified framework for addressing various types of image artifacts through a heterogeneous Mixture of Experts (MoE) architecture. Unlike traditional approaches that tackle specific artifacts individually, our model leverages specialized expert networks, each designed to handle distinct degradation patterns, while maintaining the efficiency. The heterogeneous nature of the experts allows for optimal handling of diverse artifact types, from compression artifacts to motion blur, within a single unified model.