INTERPRETABLE IMAGE RESTORATION VIA MINIMUM DESCRIPTION LENGTH ROUTING

All-in-one image restoration must balance model capacity, computational cost, and the disentanglement of degradations. Conventional Mixture-of-Experts routing relies on heuristic probabilities, producing opaque decisions and potential expert collapse.Grounded in the Minimum Description Length (MDL) principle, we propose MDLE(Minimum Description Length-routed Experts), an interpretable restoration framework that selects experts by jointly minimizing the coding length comprising model complexity and data-fitting error.Its Asymmetric Heterogeneous Expert Pool combines FFT-Attention for global frequency correction with spatial experts for local structure preservation, enabling degradation-aware on-demand computation. Experiments on restoration benchmarks show competitive reconstruction quality and efficient inference. Ablations further demonstrate that routing decisions correlate with degradation types, providing an information-theoretic account of expert selection rather than an empirical black-box gate.Although the quantitative gains over existing multi-routing approaches are currently modest, MDLE offers a distinct perspective on image denoising, dehazing, and other restoration tasks by grounding routing in an explicit information-theoretic objective. This formulation may provide a foundation for further advances and help establish a new direction for interpretable, degradation-aware image processing.