Cross-Expert Learning for All-in-One Image Restoration

Existing mixture-of-experts (MoE) methods typically assign the learner and supervisor roles to the same experts. In this paper, we introduce Cross-Expert Learning (CEL), in which each learner receives utility-weighted joint supervision from all experts. To construct this supervision, Cross-Expert Utility Estimation (CUE) evaluates all experts on shared input features to obtain candidate restoration responses. Each candidate response is scored by its local alignment with the restoration descent direction, which defines its utility. The utilities are input-dependent, varying with the degradation characteristics and image content of the current input. CUE estimates these utilities and uses them to combine the expert responses into supervision for the learner. Throughout this learning process, CEL requires neither degradation labels nor an external teacher. The additional expert responses are used only during training and do not enter the native prediction path. Experiments on standard and composite all-in-one restoration benchmarks show consistent performance improvements of CEL across different expert-based backbones, without additional model parameters or inference FLOPs.